메뉴 건너뛰기




Volumn 99, Issue , 2014, Pages 215-249

A review of novelty detection

Author keywords

Machine learning; Novelty detection; One class classification

Indexed keywords

CRITICAL SYSTEMS; EXPLICIT MODELS; MACHINE LEARNING LITERATURE; NOVELTY DETECTION; ONE-CLASS CLASSIFICATION; RESEARCH PAPERS; TEST DATA; TRAINING DATA;

EID: 84893296219     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sigpro.2013.12.026     Document Type: Review
Times cited : (1516)

References (310)
  • 2
    • 38149031678 scopus 로고    scopus 로고
    • Known unknowns novelty detection in condition monitoring
    • J. Quinn, and C. Williams Known unknowns novelty detection in condition monitoring Pattern Recognit. Image Anal. 4477 2007 1 6
    • (2007) Pattern Recognit. Image Anal. , vol.4477 , pp. 1-6
    • Quinn, J.1    Williams, C.2
  • 5
    • 77949485006 scopus 로고    scopus 로고
    • Novelty detection in a changing environment a negative selection approach
    • C. Surace, and K. Worden Novelty detection in a changing environment a negative selection approach Mech. Syst. Signal Process. 24 4 2010 1114 1128
    • (2010) Mech. Syst. Signal Process. , vol.24 , Issue.4 , pp. 1114-1128
    • Surace, C.1    Worden, K.2
  • 6
    • 34250315640 scopus 로고    scopus 로고
    • An overview of anomaly detection techniques: Existing solutions and latest technological trends
    • DOI 10.1016/j.comnet.2007.02.001, PII S138912860700062X
    • A. Patcha, and J. Park An overview of anomaly detection techniques existing solutions and latest technological trends Comput. Netw. 51 12 2007 3448 3470 (Pubitemid 46921030)
    • (2007) Computer Networks , vol.51 , Issue.12 , pp. 3448-3470
    • Patcha, A.1    Park, J.-M.2
  • 7
    • 84861874796 scopus 로고    scopus 로고
    • A review of anomaly based intrusion detection systems
    • V. Jyothsna, V.V.R. Prasad, and K.M. Prasad A review of anomaly based intrusion detection systems Int. J. Comput. Appl. 28 7 2011 26 35
    • (2011) Int. J. Comput. Appl. , vol.28 , Issue.7 , pp. 26-35
    • Jyothsna, V.1    Prasad, V.V.R.2    Prasad, K.M.3
  • 11
    • 79959563317 scopus 로고    scopus 로고
    • Anytime online novelty and change detection for mobile robots
    • B. Sofman, B. Neuman, A. Stentz, and J. Bagnell Anytime online novelty and change detection for mobile robots J. Field Robot. 28 4 2011 589 618
    • (2011) J. Field Robot. , vol.28 , Issue.4 , pp. 589-618
    • Sofman, B.1    Neuman, B.2    Stentz, A.3    Bagnell, J.4
  • 12
    • 77955082590 scopus 로고    scopus 로고
    • Outlier detection techniques for wireless sensor networks a survey
    • Y. Zhang, N. Meratnia, and P. Havinga Outlier detection techniques for wireless sensor networks a survey IEEE Commun. Surv. Tutor. 12 2 2010 159 170
    • (2010) IEEE Commun. Surv. Tutor. , vol.12 , Issue.2 , pp. 159-170
    • Zhang, Y.1    Meratnia, N.2    Havinga, P.3
  • 19
    • 68549133155 scopus 로고    scopus 로고
    • Learning from imbalanced data
    • H. He, and E. Garcia Learning from imbalanced data IEEE Trans. Knowl. Data Eng. 21 9 2009 1263 1284
    • (2009) IEEE Trans. Knowl. Data Eng. , vol.21 , Issue.9 , pp. 1263-1284
    • He, H.1    Garcia, E.2
  • 22
    • 0031169206 scopus 로고    scopus 로고
    • Outliers in statistical pattern recognition and an application to automatic chromosome classification
    • PII S0167865597000494
    • G. Ritter, and M. Gallegos Outliers in statistical pattern recognition and an application to automatic chromosome classification Pattern Recognit. Lett. 18 6 1997 525 539 (Pubitemid 127424200)
    • (1997) Pattern Recognition Letters , vol.18 , Issue.6 , pp. 525-539
    • Ritter, G.1    Gallegos, M.T.2
  • 26
    • 0142063407 scopus 로고    scopus 로고
    • Novelty detection: A review - Part 1 statistical approaches
    • M. Markou, and S. Singh Novelty detection: a review - part 1 statistical approaches Signal Process. 83 12 2003 2481 2497
    • (2003) Signal Process. , vol.83 , Issue.12 , pp. 2481-2497
    • Markou, M.1    Singh, S.2
  • 27
    • 0142126712 scopus 로고    scopus 로고
    • Novelty detection a review - Part 2: Neural network based approaches
    • M. Markou, and S. Singh Novelty detection a review - part 2: neural network based approaches Signal Process. 83 12 2003 2499 2521
    • (2003) Signal Process. , vol.83 , Issue.12 , pp. 2499-2521
    • Markou, M.1    Singh, S.2
  • 28
    • 8844281752 scopus 로고    scopus 로고
    • Novelty detection in learning systems
    • S. Marsland Novelty detection in learning systems Neural Comput. Surv. 3 2003 157 195
    • (2003) Neural Comput. Surv. , vol.3 , pp. 157-195
    • Marsland, S.1
  • 29
    • 7544223741 scopus 로고    scopus 로고
    • A survey of outlier detection methodologies
    • V. Hodge, and J. Austin A survey of outlier detection methodologies Artif. Intell. Rev. 22 2 2004 85 126
    • (2004) Artif. Intell. Rev. , vol.22 , Issue.2 , pp. 85-126
    • Hodge, V.1    Austin, J.2
  • 30
    • 47949100550 scopus 로고    scopus 로고
    • A comprehensive survey of numeric and symbolic outlier mining techniques
    • M. Agyemang, K. Barker, and R. Alhajj A comprehensive survey of numeric and symbolic outlier mining techniques Intell. Data Anal. 10 6 2006 521 538
    • (2006) Intell. Data Anal. , vol.10 , Issue.6 , pp. 521-538
    • Agyemang, M.1    Barker, K.2    Alhajj, R.3
  • 35
    • 65649091327 scopus 로고    scopus 로고
    • Analysis of time series novelty detection strategies for synthetic and real data
    • A. Modenesi, and A. Braga Analysis of time series novelty detection strategies for synthetic and real data Neural Process. Lett. 30 1 2009 1 17
    • (2009) Neural Process. Lett. , vol.30 , Issue.1 , pp. 1-17
    • Modenesi, A.1    Braga, A.2
  • 38
    • 84855359945 scopus 로고    scopus 로고
    • Anomaly, novelty, one-class classification a comprehensive introduction
    • A.M. Bartkowiak Anomaly, novelty, one-class classification a comprehensive introduction Int. J. Comput. Inf. Syst. Ind. Manage. Appl. 3 2011 61 71
    • (2011) Int. J. Comput. Inf. Syst. Ind. Manage. Appl. , vol.3 , pp. 61-71
    • Bartkowiak, A.M.1
  • 42
    • 79953319913 scopus 로고    scopus 로고
    • Data mining based network intrusion detection system a survey
    • R. Helali Data mining based network intrusion detection system a survey Novel Algoritm. Tech. Telecommun. Netw. 2010 501 505
    • (2010) Novel Algoritm. Tech. Telecommun. Netw. , pp. 501-505
    • Helali, R.1
  • 43
    • 84946031884 scopus 로고
    • Procedures for detecting outlying observations in samples
    • F.E. Grubbs Procedures for detecting outlying observations in samples Technometrics 11 1 1969 1 21
    • (1969) Technometrics , vol.11 , Issue.1 , pp. 1-21
    • Grubbs, F.E.1
  • 45
    • 28044457628 scopus 로고    scopus 로고
    • Detection of outliers in reference distributions: Performance of horn's algorithm
    • DOI 10.1373/clinchem.2005.058339
    • H. Solberg, and A. Lahti Detection of outliers in reference distributions performance of Horn's algorithm Clin. Chem. 51 12 2005 2326 2332 (Pubitemid 41692577)
    • (2005) Clinical Chemistry , vol.51 , Issue.12 , pp. 2326-2332
    • Solberg, H.E.1    Lahti, A.2
  • 46
    • 0014710323 scopus 로고
    • On optimum recognition error and reject tradeoff
    • C. Chow On optimum recognition error and reject tradeoff IEEE Trans. Inf. Theory 16 1 1970 41 46
    • (1970) IEEE Trans. Inf. Theory , vol.16 , Issue.1 , pp. 41-46
    • Chow, C.1
  • 47
    • 84893268965 scopus 로고    scopus 로고
    • John Wiley & Sons, Inc.
    • D.W. Scott Frontmatter 2008 John Wiley & Sons, Inc.
    • (2008) Frontmatter
    • Scott, D.W.1
  • 51
    • 34047236024 scopus 로고    scopus 로고
    • Gaussian mixture pdf in one-class classification: Computing and utilizing confidence values
    • DOI 10.1109/ICPR.2006.595, 1699271, Proceedings - 18th International Conference on Pattern Recognition, ICPR 2006
    • J. Ilonen, P. Paalanen, J. Kamarainen, H. Kalviainen, Gaussian mixture pdf in one-class classification: computing and utilizing confidence values, in: Proceedings of the 18th International Conference on Pattern Recognition (ICPR), vol. 2, IEEE, 2006, pp. 577-580. (Pubitemid 46532265)
    • (2006) Proceedings - International Conference on Pattern Recognition , vol.2 , pp. 577-580
    • Ilonen, J.1    Paalanen, P.2    Kamarainen, J.-K.3    Kalviainen, H.4
  • 53
    • 33646093001 scopus 로고    scopus 로고
    • Feature representation and discrimination based on Gaussian mixture model probability densities - Practices and algorithms
    • P. Paalanen, J. Kamarainen, J. Ilonen, and H. Kälviäinen Feature representation and discrimination based on Gaussian mixture model probability densities - practices and algorithms Pattern Recognit. 39 7 2006 1346 1358
    • (2006) Pattern Recognit. , vol.39 , Issue.7 , pp. 1346-1358
    • Paalanen, P.1    Kamarainen, J.2    Ilonen, J.3    Kälviäinen, H.4
  • 59
    • 81855226144 scopus 로고    scopus 로고
    • Novelty detection with multivariate extreme value statistics
    • D. Clifton, S. Hugueny, and L. Tarassenko Novelty detection with multivariate extreme value statistics J. Signal Process. Syst. 65 3 2011 371 389
    • (2011) J. Signal Process. Syst. , vol.65 , Issue.3 , pp. 371-389
    • Clifton, D.1    Hugueny, S.2    Tarassenko, L.3
  • 64
    • 0033359542 scopus 로고    scopus 로고
    • Novelty detection using extreme value statistics
    • DOI 10.1049/ip-vis:19990428
    • S. Roberts, Novelty detection using extreme value statistics, in: Proceedings of the IEEE Conference on Vision, Image and Signal Processing 146 (3) (1999) 124-129. (Pubitemid 30500232)
    • (1999) IEE Proceedings: Vision, Image and Signal Processing , vol.146 , Issue.3 , pp. 124-129
    • Roberts, S.J.1
  • 65
    • 0034313895 scopus 로고    scopus 로고
    • Extreme value statistics for novelty detection in biomedical data processing
    • DOI 10.1049/ip-smt:20000841
    • S. Roberts, Extreme value statistics for novelty detection in biomedical data processing, in: Proceedings of the IEEE Conference on Science, Measurement and Technology, vol. 147, IET, 2000, pp. 363-367. (Pubitemid 32134375)
    • (2000) IEE Proceedings: Science, Measurement and Technology , vol.147 , Issue.6 , pp. 363-367
    • Roberts, S.J.1
  • 73
  • 75
    • 79960670476 scopus 로고    scopus 로고
    • Probabilistic novelty detection for acoustic surveillance under real-world conditions
    • S. Ntalampiras, I. Potamitis, and N. Fakotakis Probabilistic novelty detection for acoustic surveillance under real-world conditions IEEE Trans. Multimed. 13 4 2011 713 719
    • (2011) IEEE Trans. Multimed. , vol.13 , Issue.4 , pp. 713-719
    • Ntalampiras, S.1    Potamitis, I.2    Fakotakis, N.3
  • 76
    • 80054796335 scopus 로고    scopus 로고
    • Novelty detection using graphical models for semantic room classification
    • A. Pinto, A. Pronobis, and L. Reis Novelty detection using graphical models for semantic room classification Prog. Artif. Intell. 7026 2011 326 339
    • (2011) Prog. Artif. Intell. , vol.7026 , pp. 326-339
    • Pinto, A.1    Pronobis, A.2    Reis, L.3
  • 77
    • 0037142572 scopus 로고    scopus 로고
    • Anomaly intrusion detection method based on HMM
    • DOI 10.1049/el:20020467
    • Y. Qiao, X. Xin, Y. Bin, and S. Ge Anomaly intrusion detection method based on HMM Electron. Lett. 38 13 2002 663 664 (Pubitemid 34725625)
    • (2002) Electronics Letters , vol.38 , Issue.13 , pp. 663-664
    • Qiao, Y.1    Xin, X.W.2    Bin, Y.3    Ge, S.4
  • 78
    • 67650995767 scopus 로고    scopus 로고
    • Factorial switching linear dynamical systems applied to physiological condition monitoring
    • J. Quinn, C. Williams, and N. McIntosh Factorial switching linear dynamical systems applied to physiological condition monitoring IEEE Trans. Pattern Anal. Mach. Intell. 31 9 2009 1537 1551
    • (2009) IEEE Trans. Pattern Anal. Mach. Intell. , vol.31 , Issue.9 , pp. 1537-1551
    • Quinn, J.1    Williams, C.2    McIntosh, N.3
  • 80
    • 84864058259 scopus 로고    scopus 로고
    • Factorial switching Kalman filters for condition monitoring in neonatal intensive care
    • C. Williams, J. Quinn, and N. McIntosh Factorial switching Kalman filters for condition monitoring in neonatal intensive care Neural Inf. Process. 2006 1513 1520
    • (2006) Neural Inf. Process. , pp. 1513-1520
    • Williams, C.1    Quinn, J.2    McIntosh, N.3
  • 81
    • 0036923209 scopus 로고    scopus 로고
    • Rule-based anomaly pattern detection for detecting disease outbreaks
    • Menlo Park, CA; Cambridge, MA; London, AAAI Press; MIT Press; 1999
    • W. Wong, A. Moore, G. Cooper, M. Wagner, Rule-based anomaly pattern detection for detecting disease outbreaks, in: Proceedings of the National Conference on Artificial Intelligence, Menlo Park, CA; Cambridge, MA; London, AAAI Press; MIT Press; 1999, 2002, pp. 217-223.
    • (2002) Proceedings of the National Conference on Artificial Intelligence , pp. 217-223
    • Wong, W.1    Moore, A.2    Cooper, G.3    Wagner, M.4
  • 83
    • 0037209446 scopus 로고    scopus 로고
    • Host-based intrusion detection using dynamic and static behavioral models
    • D.-Y. Yeung, and Y. Ding Host-based intrusion detection using dynamic and static behavioral models Pattern Recognit. 36 1 2003 229 243
    • (2003) Pattern Recognit. , vol.36 , Issue.1 , pp. 229-243
    • Yeung, D.-Y.1    Ding, Y.2
  • 85
    • 1142279663 scopus 로고    scopus 로고
    • An approach for fuzzy rule-base adaptation using on-line clustering
    • P. Angelov An approach for fuzzy rule-base adaptation using on-line clustering Int. J. Approx. Reason. 35 3 2004 275 289
    • (2004) Int. J. Approx. Reason. , vol.35 , Issue.3 , pp. 275-289
    • Angelov, P.1
  • 95
    • 33745713040 scopus 로고    scopus 로고
    • Integrated monitoring and analysis for early warning of patient deterioration
    • L. Tarassenko, A. Hann, and D. Young Integrated monitoring and analysis for early warning of patient deterioration Br. J. Anaesth. 97 1 2006 64 68
    • (2006) Br. J. Anaesth. , vol.97 , Issue.1 , pp. 64-68
    • Tarassenko, L.1    Hann, A.2    Young, D.3
  • 101
    • 3543106606 scopus 로고    scopus 로고
    • Anomaly detection using real-valued negative selection
    • DOI 10.1023/A:1026195112518
    • F. González, and D. Dasgupta Anomaly detection using real-valued negative selection Genet. Program. Evolvable Mach. 4 4 2003 383 403 (Pubitemid 37283494)
    • (2003) Genetic Programming And Evolvable Machines , vol.4 , Issue.4 , pp. 383-403
    • Gonzalez, F.A.1    Dasgupta, D.2
  • 102
    • 35248825596 scopus 로고    scopus 로고
    • An investigation of the negative selection algorithm for fault detection in refrigeration systems
    • D. Taylor, and D. Corne An investigation of the negative selection algorithm for fault detection in refrigeration systems Artif. Immune Syst. 2787 2003 34 45
    • (2003) Artif. Immune Syst. , vol.2787 , pp. 34-45
    • Taylor, D.1    Corne, D.2
  • 105
    • 27544508958 scopus 로고    scopus 로고
    • A gamma mixture model better accounts for among site rate heterogeneity
    • I. Mayrose, N. Friedman, and T. Pupko A gamma mixture model better accounts for among site rate heterogeneity Bioinformatics 21 2 2005 151 158
    • (2005) Bioinformatics , vol.21 , Issue.2 , pp. 151-158
    • Mayrose, I.1    Friedman, N.2    Pupko, T.3
  • 106
    • 34247850854 scopus 로고    scopus 로고
    • Modelling nonlinear count time series with local mixtures of Poisson autoregressions
    • DOI 10.1016/j.csda.2006.09.032, PII S0167947306003586
    • A. Carvalho, and M. Tanner Modelling nonlinear count time series with local mixtures of poisson autoregressions Comput. Stat. Data Anal. 51 11 2007 5266 5294 (Pubitemid 46694026)
    • (2007) Computational Statistics and Data Analysis , vol.51 , Issue.11 , pp. 5266-5294
    • Carvalho, A.X.1    Tanner, M.A.2
  • 107
    • 15844362098 scopus 로고    scopus 로고
    • Robust Bayesian mixture modelling
    • DOI 10.1016/j.neucom.2004.11.018, PII S0925231204005181
    • M. Svensén, and C. Bishop Robust Bayesian mixture modelling Neurocomputing 64 2005 235 252 (Pubitemid 40425321)
    • (2005) Neurocomputing , vol.64 , Issue.1-4 SPEC. ISS. , pp. 235-252
    • Svensen, M.1    Bishop, C.M.2
  • 109
    • 0000184052 scopus 로고    scopus 로고
    • Statistical independence and novelty detection with information preserving nonlinear maps
    • L. Parra, G. Deco, and S. Miesbach Statistical independence and novelty detection with information preserving nonlinear maps Neural Comput. 8 2 1996 260 269 (Pubitemid 126449927)
    • (1996) Neural Computation , vol.8 , Issue.2 , pp. 260-269
    • Parra, L.1    Deco, G.2    Miesbach, S.3
  • 111
    • 0030327833 scopus 로고    scopus 로고
    • Computing and graphing highest density regions
    • R. Hyndman Computing and graphing highest density regions Am. Stat. 50 2 1996 120 126
    • (1996) Am. Stat. , vol.50 , Issue.2 , pp. 120-126
    • Hyndman, R.1
  • 112
    • 0001075431 scopus 로고
    • Statistical inference using extreme order statistics
    • J. Pickands Statistical inference using extreme order statistics Ann. Stat. 3 1 1975 119 131
    • (1975) Ann. Stat. , vol.3 , Issue.1 , pp. 119-131
    • Pickands, J.1
  • 114
    • 84958156266 scopus 로고
    • Limiting forms of the frequency distribution of the largest or smallest member of a sample
    • Cambridge University Press
    • R. Fisher, L. Tippett, Limiting forms of the frequency distribution of the largest or smallest member of a sample, in: Proceedings of the Cambridge Philosophical Society, vol. 24, Cambridge University Press, 1928, pp. 180-190.
    • (1928) Proceedings of the Cambridge Philosophical Society , vol.24 , pp. 180-190
    • Fisher, R.1    Tippett, L.2
  • 116
    • 0037248049 scopus 로고    scopus 로고
    • Experimental validation of a structural health monitoring methodology part i. Novelty detection on a laboratory structure
    • K. Worden, G. Manson, and D. Allman Experimental validation of a structural health monitoring methodology part i. Novelty detection on a laboratory structure J. Sound Vib. 259 2 2003 323 343
    • (2003) J. Sound Vib. , vol.259 , Issue.2 , pp. 323-343
    • Worden, K.1    Manson, G.2    Allman, D.3
  • 117
    • 3543125360 scopus 로고    scopus 로고
    • On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms
    • DOI 10.1023/B:DAMI.0000023676.72185.7c
    • K. Yamanishi, J. Takeuchi, G. Williams, and P. Milne On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms Data Min. Knowl. Discov. 8 3 2004 275 300 (Pubitemid 39019964)
    • (2004) Data Mining and Knowledge Discovery , vol.8 , Issue.3 , pp. 275-300
    • Yamanishi, K.1    Takeuchi, J.-I.2    Williams, G.3    Milne, P.4
  • 119
    • 34548577511 scopus 로고    scopus 로고
    • An Empirical Bayes approach to detect anomalies in dynamic multidimensional arrays
    • DOI 10.1109/ICDM.2005.22, 1565658, Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005
    • D. Agarwal, An empirical Bayes approach to detect anomalies in dynamic multidimensional arrays, in: Proceedings of the 5th IEEE International Conference on Data Mining, IEEE, 2005, pp. 26-33. (Pubitemid 47385673)
    • (2005) Proceedings - IEEE International Conference on Data Mining, ICDM , pp. 26-33
    • Agarwal, D.1
  • 120
    • 33845323774 scopus 로고    scopus 로고
    • Detecting anomalies in cross-classified streams: A Bayesian approach
    • DOI 10.1007/s10115-006-0036-4
    • D. Agarwal Detecting anomalies in cross-classified streams a Bayesian approach Knowl. Inf. Syst. 11 1 2007 29 44 (Pubitemid 44867020)
    • (2007) Knowledge and Information Systems , vol.11 , Issue.1 , pp. 29-44
    • Agarwal, D.1
  • 121
    • 0037349382 scopus 로고    scopus 로고
    • Using novelty detection to identify abnormalities caused by mean shifts in bivariate processes
    • F. Zorriassatine, J. Tannock, and C. O'Brien Using novelty detection to identify abnormalities caused by mean shifts in bivariate processes Comput. Ind. Eng. 44 3 2003 385 408
    • (2003) Comput. Ind. Eng. , vol.44 , Issue.3 , pp. 385-408
    • Zorriassatine, F.1    Tannock, J.2    O'Brien, C.3
  • 122
    • 0001387715 scopus 로고    scopus 로고
    • Mean-field approaches to independent component analysis
    • P. Højen-Sørensen, O. Winther, and L. Hansen Mean-field approaches to independent component analysis Neural Comput. 14 4 2002 889 918
    • (2002) Neural Comput. , vol.14 , Issue.4 , pp. 889-918
    • Højen-Sørensen, P.1    Winther, O.2    Hansen, L.3
  • 123
    • 0037317897 scopus 로고    scopus 로고
    • Efficient greedy learning of gaussian mixture models
    • DOI 10.1162/089976603762553004
    • J. Verbeek, N. Vlassis, and B. Kröse Efficient greedy learning of Gaussian mixture models Neural Comput. 15 2 2003 469 485 (Pubitemid 37049830)
    • (2003) Neural Computation , vol.15 , Issue.2 , pp. 469-485
    • Verbeek, J.J.1    Vlassis, N.2    Krose, B.3
  • 124
    • 84898950747 scopus 로고    scopus 로고
    • A probabilistic model for online document clustering with application to novelty detection
    • J. Zhang, Z. Ghahramani, Y. Yang, A probabilistic model for online document clustering with application to novelty detection, in: NIPS, 2005.
    • (2005) NIPS
    • Zhang, J.1    Ghahramani, Z.2    Yang, Y.3
  • 125
    • 58249143167 scopus 로고    scopus 로고
    • Concepts for novelty detection and handling based on a case-based reasoning process scheme
    • P. Perner Concepts for novelty detection and handling based on a case-based reasoning process scheme Eng. Appl. Artif. Intell. 22 1 2009 86 91
    • (2009) Eng. Appl. Artif. Intell. , vol.22 , Issue.1 , pp. 86-91
    • Perner, P.1
  • 126
    • 56049119915 scopus 로고    scopus 로고
    • One-class classification by combining density and class probability estimation
    • W. Daelemans, B. Goethals, K. Morik, Springer Berlin/Heidelberg
    • K. Hempstalk, E. Frank, and I. Witten One-class classification by combining density and class probability estimation W. Daelemans, B. Goethals, K. Morik, Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science vol. 5211 2008 Springer Berlin/Heidelberg 505 519
    • (2008) Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science , vol.5211 VOL. , pp. 505-519
    • Hempstalk, K.1    Frank, E.2    Witten, I.3
  • 128
    • 68949141755 scopus 로고    scopus 로고
    • A least-squares approach to direct importance estimation
    • T. Kanamori, S. Hido, and M. Sugiyama A least-squares approach to direct importance estimation J. Mach. Learn. Res. 10 2009 1391 1445
    • (2009) J. Mach. Learn. Res. , vol.10 , pp. 1391-1445
    • Kanamori, T.1    Hido, S.2    Sugiyama, M.3
  • 129
    • 78651496720 scopus 로고    scopus 로고
    • Statistical outlier detection using direct density ratio estimation
    • S. Hido, Y. Tsuboi, H. Kashima, M. Sugiyama, and T. Kanamori Statistical outlier detection using direct density ratio estimation Knowl. Inf. Syst. 26 2 2011 309 336
    • (2011) Knowl. Inf. Syst. , vol.26 , Issue.2 , pp. 309-336
    • Hido, S.1    Tsuboi, Y.2    Kashima, H.3    Sugiyama, M.4    Kanamori, T.5
  • 130
    • 79251621795 scopus 로고    scopus 로고
    • Density ratio estimation a comprehensive review
    • M. Sugiyama, T. Suzuki, and T. Kanamori Density ratio estimation a comprehensive review RIMS Kokyuroku 2010 10 31
    • (2010) RIMS Kokyuroku , pp. 10-31
    • Sugiyama, M.1    Suzuki, T.2    Kanamori, T.3
  • 131
    • 0036891410 scopus 로고    scopus 로고
    • On-line novelty detection for artefact identification in automatic anaesthesia record keeping
    • DOI 10.1016/S1350-4533(02)00146-7, PII S1350453302001467
    • S. Hoare, D. Asbridge, and P. Beatty On-line novelty detection for artefact identification in automatic anaesthesia record keeping Med. Eng. Phys. 24 10 2002 673 681 (Pubitemid 35379944)
    • (2002) Medical Engineering and Physics , vol.24 , Issue.10 , pp. 673-681
    • Hoare, S.W.1    Asbridge, D.2    Beatty, P.C.W.3
  • 132
    • 0001614845 scopus 로고
    • A probabilistic resource allocating network for novelty detection
    • S. Roberts, and L. Tarassenko A probabilistic resource allocating network for novelty detection Neural Comput. 6 2 1994 270 284
    • (1994) Neural Comput. , vol.6 , Issue.2 , pp. 270-284
    • Roberts, S.1    Tarassenko, L.2
  • 133
    • 33745652986 scopus 로고    scopus 로고
    • Outlier detection in multivariate time series by projection pursuit
    • DOI 10.1198/016214505000001131
    • P. Galeano, D. Peña, and R. Tsay Outlier detection in multivariate time series by projection pursuit J. Am. Stat. Assoc. 101 474 2006 654 669 (Pubitemid 43972306)
    • (2006) Journal of the American Statistical Association , vol.101 , Issue.474 , pp. 654-669
    • Galeano, P.1    Pena, D.2    Tsay, R.S.3
  • 134
    • 14644414808 scopus 로고    scopus 로고
    • Simultaneous wavelength selection and outlier detection in multivariate regression of near-infrared spectra
    • DOI 10.2116/analsci.21.161
    • D. Chen, X. Shao, B. Hu, and Q. Su Simultaneous wavelength selection and outlier detection in multivariate regression of near-infrared spectra Anal. Sci. 21 2 2005 161 166 (Pubitemid 40313909)
    • (2005) Analytical Sciences , vol.21 , Issue.2 , pp. 161-166
    • Chen, D.1    Shao, X.2    Hu, B.3    Su, Q.4
  • 135
    • 33748703576 scopus 로고    scopus 로고
    • Detecting outlying samples in microarray data: A critical assessment of the effect of outliers on sample classification
    • K. Kadota, D. Tominaga, Y. Akiyama, and K. Takahashi Detecting outlying samples in microarray data: a critical assessment of the effect of outliers on sample classification Chem-Bio Informat. 3 1 2003 30 45
    • (2003) Chem-Bio Informat. , vol.3 , Issue.1 , pp. 30-45
    • Kadota, K.1    Tominaga, D.2    Akiyama, Y.3    Takahashi, K.4
  • 136
    • 0028724758 scopus 로고
    • Markov monitoring with unknown states
    • P. Smyth Markov monitoring with unknown states IEEE J. Sel. Areas Commun. 12 9 1994 1600 1612
    • (1994) IEEE J. Sel. Areas Commun. , vol.12 , Issue.9 , pp. 1600-1612
    • Smyth, P.1
  • 137
    • 0034170950 scopus 로고    scopus 로고
    • Variational learning for switching state-space models
    • Z. Ghahramani, and G. Hinton Variational learning for switching state-space models Neural Comput. 12 4 2000 831 864
    • (2000) Neural Comput. , vol.12 , Issue.4 , pp. 831-864
    • Ghahramani, Z.1    Hinton, G.2
  • 139
    • 26844565732 scopus 로고    scopus 로고
    • Active platform security through intrusion detection using naive Bayesian network for anomaly detection
    • A. Sebyala, T. Olukemi, L. Sacks, Active platform security through intrusion detection using naive Bayesian network for anomaly detection, in: London Communications Symposium, Citeseer, 2002.
    • (2002) London Communications Symposium, Citeseer
    • Sebyala, A.1    Olukemi, T.2    Sacks, L.3
  • 143
    • 0001473437 scopus 로고
    • On estimation of a probability density function and mode
    • E. Parzen On estimation of a probability density function and mode Ann. Math. Stat. 33 3 1962 1065 1076
    • (1962) Ann. Math. Stat. , vol.33 , Issue.3 , pp. 1065-1076
    • Parzen, E.1
  • 145
    • 46449103765 scopus 로고    scopus 로고
    • Defining the incidence of cardiorespiratory instability in patients in step-down units using an electronic integrated monitoring system
    • DOI 10.1001/archinte.168.12.1300
    • M. Hravnak, L. Edwards, A. Clontz, C. Valenta, M. DeVita, and M. Pinsky Defining the incidence of cardiorespiratory instability in patients in step-down units using an electronic integrated monitoring system Arch. Internal Med. 168 12 2008 1300 1308 (Pubitemid 351930837)
    • (2008) Archives of Internal Medicine , vol.168 , Issue.12 , pp. 1300-1308
    • Hravnak, M.1    Edwards, L.2    Clontz, A.3    Valenta, C.4    DeVita, M.A.5    Pinsky, M.R.6
  • 146
    • 0742272554 scopus 로고    scopus 로고
    • An approach to online identification of Takagi-Sugeno fuzzy models
    • P. Angelov, and D. Filev An approach to online identification of Takagi-Sugeno fuzzy models IEEE Trans. Syst. Man Cybern. Part B Cybern. 34 1 2004 484 498
    • (2004) IEEE Trans. Syst. Man Cybern. Part B Cybern. , vol.34 , Issue.1 , pp. 484-498
    • Angelov, P.1    Filev, D.2
  • 148
    • 0001524507 scopus 로고
    • Procedures for reacting to a change in distribution
    • G. Lorden Procedures for reacting to a change in distribution Ann. Math. Stat. 42 6 1971 1897 1908
    • (1971) Ann. Math. Stat. , vol.42 , Issue.6 , pp. 1897-1908
    • Lorden, G.1
  • 151
    • 33947689693 scopus 로고    scopus 로고
    • Information sharing for distributed intrusion detection systems
    • DOI 10.1016/j.jnca.2005.07.004, PII S1084804505000494
    • T. Peng, C. Leckie, and K. Ramamohanarao Information sharing for distributed intrusion detection systems J. Netw. Comput. Appl. 30 3 2007 877 899 (Pubitemid 46497019)
    • (2007) Journal of Network and Computer Applications , vol.30 , Issue.3 , pp. 877-899
    • Peng, T.1    Leckie, C.2    Ramamohanarao, K.3
  • 153
    • 77952579459 scopus 로고    scopus 로고
    • State-Of-The-Art in Bayesian changepoint detection
    • A.G. Tartakovsky, and G.V. Moustakides State-of-the-art in Bayesian changepoint detection Seq. Anal. 29 2 2010 125 145
    • (2010) Seq. Anal. , vol.29 , Issue.2 , pp. 125-145
    • Tartakovsky, A.G.1    Moustakides, G.V.2
  • 161
    • 33645548899 scopus 로고    scopus 로고
    • SLOM: A new measure for local spatial outliers
    • DOI 10.1007/s10115-005-0200-2
    • S. Chawla, and P. Sun SLOM a new measure for local spatial outliers Knowl. Inf. Syst. 9 4 2006 412 429 (Pubitemid 43507459)
    • (2006) Knowledge and Information Systems , vol.9 , Issue.4 , pp. 412-429
    • Chawla, S.1    Sun, P.2
  • 163
    • 42749086305 scopus 로고    scopus 로고
    • Fast mining of distance-based outliers in high-dimensional datasets
    • A. Ghoting, S. Parthasarathy, and M. Otey Fast mining of distance-based outliers in high-dimensional datasets Data Min. Knowl. Discov. 16 3 2008 349 364
    • (2008) Data Min. Knowl. Discov. , vol.16 , Issue.3 , pp. 349-364
    • Ghoting, A.1    Parthasarathy, S.2    Otey, M.3
  • 167
    • 33646553013 scopus 로고    scopus 로고
    • Fast distributed outlier detection in mixed-attribute data sets
    • M. Otey, A. Ghoting, and S. Parthasarathy Fast distributed outlier detection in mixed-attribute data sets Data Min. Knowl. Discov. 12 2 2006 203 228
    • (2006) Data Min. Knowl. Discov. , vol.12 , Issue.2 , pp. 203-228
    • Otey, M.1    Ghoting, A.2    Parthasarathy, S.3
  • 171
    • 33749316703 scopus 로고    scopus 로고
    • Detecting outlying subspaces for high-dimensional data: The new task, algorithms, and performance
    • DOI 10.1007/s10115-006-0020-z
    • J. Zhang, and H. Wang Detecting outlying subspaces for high-dimensional data the new task, and performance Knowl. Inf. Syst. 10 3 2006 333 355 (Pubitemid 44490000)
    • (2006) Knowledge and Information Systems , vol.10 , Issue.3 , pp. 333-355
    • Zhang, J.1    Wang, H.2
  • 176
    • 34250804775 scopus 로고    scopus 로고
    • A framework for novelty detection in jet engine vibration data
    • Damage Assessment of Structures VII
    • D. Clifton, P. Bannister, and L. Tarassenko A framework for novelty detection in jet engine vibration data Key Eng. Mater. 347 2007 305 310 (Pubitemid 46987334)
    • (2007) Key Engineering Materials , vol.347 , pp. 305-310
    • Clifton, D.A.1    Bannister, P.R.2    Tarassenko, L.3
  • 177
    • 77953110999 scopus 로고    scopus 로고
    • Applying the possibilistic c-means algorithm in kernel-induced spaces
    • M. Filippone, F. Masulli, and S. Rovetta Applying the possibilistic c-means algorithm in kernel-induced spaces IEEE Trans. Fuzzy Syst. 18 3 2010 572 584
    • (2010) IEEE Trans. Fuzzy Syst. , vol.18 , Issue.3 , pp. 572-584
    • Filippone, M.1    Masulli, F.2    Rovetta, S.3
  • 178
    • 0037410488 scopus 로고    scopus 로고
    • Discovering cluster-based local outliers
    • Z. He, X. Xu, and S. Deng Discovering cluster-based local outliers Pattern Recognit. Lett. 24 9 2003 1641 1650
    • (2003) Pattern Recognit. Lett. , vol.24 , Issue.9 , pp. 1641-1650
    • He, Z.1    Xu, X.2    Deng, S.3
  • 179
    • 82255179131 scopus 로고    scopus 로고
    • Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing
    • D. Kim, P. Kang, S. Cho, H. Lee, and S. Doh Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing Expert Syst. Appl. 39 4 2011 4075 4083
    • (2011) Expert Syst. Appl. , vol.39 , Issue.4 , pp. 4075-4083
    • Kim, D.1    Kang, P.2    Cho, S.3    Lee, H.4    Doh, S.5
  • 181
    • 70349676147 scopus 로고    scopus 로고
    • Enabling the discovery of recurring anomalies in aerospace problem reports using high-dimensional clustering techniques
    • A. Srivastava, Enabling the discovery of recurring anomalies in aerospace problem reports using high-dimensional clustering techniques, in: Proceedings of the IEEE Aerospace Conference, IEEE, 2006, pp. 1-17.
    • (2006) Proceedings of the IEEE Aerospace Conference, IEEE , pp. 1-17
    • Srivastava, A.1
  • 182
    • 35048823911 scopus 로고    scopus 로고
    • CD-trees an efficient index structure for outlier detection
    • H. Sun, Y. Bao, F. Zhao, G. Yu, and D. Wang CD-trees an efficient index structure for outlier detection Adv. Web-Age Inf. Manage. 3129 2004 600 609
    • (2004) Adv. Web-Age Inf. Manage. , vol.3129 , pp. 600-609
    • Sun, H.1    Bao, Y.2    Zhao, F.3    Yu, G.4    Wang, D.5
  • 184
    • 56349122418 scopus 로고    scopus 로고
    • Outlier identification and market segmentation using kernel-based clustering techniques
    • C.-H. Wang Outlier identification and market segmentation using kernel-based clustering techniques Exp. Syst. Appl. 36 2 2009 3744 3750
    • (2009) Exp. Syst. Appl. , vol.36 , Issue.2 , pp. 3744-3750
    • Wang, C.-H.1
  • 186
    • 84861610344 scopus 로고    scopus 로고
    • Novelty detection in wildlife scenes through semantic context modelling
    • S.-P. Yong, J.D. Deng, and M.K. Purvis Novelty detection in wildlife scenes through semantic context modelling Pattern Recognit. 45 9 2012 3439 3450
    • (2012) Pattern Recognit. , vol.45 , Issue.9 , pp. 3439-3450
    • Yong, S.-P.1    Deng, J.D.2    Purvis, M.K.3
  • 187
    • 84893219608 scopus 로고    scopus 로고
    • Wildlife video key-frame extraction based on novelty detection in semantic context
    • S. Yong, J. Deng, and M. Purvis Wildlife video key-frame extraction based on novelty detection in semantic context Multimed. Tools Appl. 62 2 2013 359 376
    • (2013) Multimed. Tools Appl. , vol.62 , Issue.2 , pp. 359-376
    • Yong, S.1    Deng, J.2    Purvis, M.3
  • 188
    • 85132247975 scopus 로고    scopus 로고
    • Findout finding outliers in very large datasets
    • D. Yu, G. Sheikholeslami, and A. Zhang Findout finding outliers in very large datasets Knowl. Inf. Syst. 4 4 2002 387 412
    • (2002) Knowl. Inf. Syst. , vol.4 , Issue.4 , pp. 387-412
    • Yu, D.1    Sheikholeslami, G.2    Zhang, A.3
  • 189
    • 38049044112 scopus 로고    scopus 로고
    • Unsupervised outlier detection in sensor networks using aggregation tree
    • K. Zhang, S. Shi, H. Gao, and J. Li Unsupervised outlier detection in sensor networks using aggregation tree Adv. Data Min. Appl. 4632 2007 158 169
    • (2007) Adv. Data Min. Appl. , vol.4632 , pp. 158-169
    • Zhang, K.1    Shi, S.2    Gao, H.3    Li, J.4
  • 191
    • 33749567862 scopus 로고    scopus 로고
    • Mining distance-based outliers from large databases in any metric space
    • KDD 2006: Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    • Y. Tao, X. Xiao, S. Zhou, Mining distance-based outliers from large databases in any metric space, in: Proceedings of the 12th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), ACM, 2006, pp. 394-403. (Pubitemid 44535536)
    • (2006) Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , vol.2006 , pp. 394-403
    • Tao, Y.1    Xiao, X.2    Zhou, S.3
  • 196
    • 84945281435 scopus 로고    scopus 로고
    • Enhancing effectiveness of outlier detections for low density patterns
    • J. Tang, Z. Chen, A. Fu, and D. Cheung Enhancing effectiveness of outlier detections for low density patterns Adv. Knowl. Discov. Data Min. 2336 2002 535 548
    • (2002) Adv. Knowl. Discov. Data Min. , vol.2336 , pp. 535-548
    • Tang, J.1    Chen, Z.2    Fu, A.3    Cheung, D.4
  • 197
    • 19544366584 scopus 로고    scopus 로고
    • RDF: A density-based outlier detection method using vertical data representation
    • Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
    • D. Ren, B. Wang, W. Perrizo, RDF: a density-based outlier detection method using vertical data representation, in: Proceedings of the 4th IEEE International Conference on Data Mining, ICDM'04, IEEE, 2004, pp. 503-506. (Pubitemid 40731093)
    • (2004) Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004 , pp. 503-506
    • Ren, D.1    Wang, B.2    Perrizo, W.3
  • 198
    • 33645552151 scopus 로고    scopus 로고
    • Finding centric local outliers in categorical/numerical spaces
    • DOI 10.1007/s10115-005-0197-6
    • J. Yu, W. Qian, H. Lu, and A. Zhou Finding centric local outliers in categorical/numerical spaces Knowl. Inf. Syst. 9 3 2006 309 338 (Pubitemid 43507136)
    • (2006) Knowledge and Information Systems , vol.9 , Issue.3 , pp. 309-338
    • Yu, J.X.1    Qian, W.2    Lu, H.3    Zhou, A.4
  • 199
    • 33845240405 scopus 로고    scopus 로고
    • Capabilities of outlier detection schemes in large datasets, framework and methodologies
    • DOI 10.1007/s10115-005-0233-6
    • J. Tang, Z. Chen, A. Fu, and D. Cheung Capabilities of outlier detection in large datasets, framework and methodologies Knowl. Inf. Syst. 11 1 2007 45 84 (Pubitemid 44857542)
    • (2007) Knowledge and Information Systems , vol.11 , Issue.1 , pp. 45-84
    • Tang, J.1    Chen, Z.2    Fu, A.W.3    Cheung, D.W.4
  • 204
  • 208
    • 40349086675 scopus 로고    scopus 로고
    • Finding topics in collections of documents a shared nearest neighbor approach
    • L. Ertöz, M. Steinbach, and V. Kumar Finding topics in collections of documents a shared nearest neighbor approach Clust. Inf. Retr. 11 2003 83 103
    • (2003) Clust. Inf. Retr. , vol.11 , pp. 83-103
    • Ertöz, L.1    Steinbach, M.2    Kumar, V.3
  • 210
    • 79957676241 scopus 로고    scopus 로고
    • Unsupervised distributed novelty detection on scientific simulation data
    • J. Zhou, Y. Fu, C. Sun, and Y. Fang Unsupervised distributed novelty detection on scientific simulation data J. Comput. Inf. Syst. 7 5 2011 1533 1540
    • (2011) J. Comput. Inf. Syst. , vol.7 , Issue.5 , pp. 1533-1540
    • Zhou, J.1    Fu, Y.2    Sun, C.3    Fang, Y.4
  • 215
    • 0037143140 scopus 로고    scopus 로고
    • Neural network classification and novelty detection
    • DOI 10.1080/01431160110055804
    • M. Augusteijn, and B. Folkert Neural network classification and novelty detection Int. J. Remote Sens. 23 14 2002 2891 2902 (Pubitemid 34825152)
    • (2002) International Journal of Remote Sensing , vol.23 , Issue.14 , pp. 2891-2902
    • Augusteijn, M.F.1    Folkert, B.A.2
  • 216
    • 2142828547 scopus 로고    scopus 로고
    • An approach to novelty detection applied to the classification of image regions
    • S. Singh, and M. Markou An approach to novelty detection applied to the classification of image regions IEEE Trans. Knowl. Data Eng. 16 4 2004 396 407
    • (2004) IEEE Trans. Knowl. Data Eng. , vol.16 , Issue.4 , pp. 396-407
    • Singh, S.1    Markou, M.2
  • 218
    • 0036082925 scopus 로고    scopus 로고
    • Residual generation and visualization for understanding novel process conditions
    • I. Diaz, J. Hollmen, Residual generation and visualization for understanding novel process conditions, in: Proceedings of the International Joint Conference on Neural Networks, IJCNN'02, vol. 3, IEEE, 2002, pp. 2070-2075. (Pubitemid 34647950)
    • (2002) Proceedings of the International Joint Conference on Neural Networks , vol.3 , pp. 2070-2075
    • Diaz, I.1    Hollmen, J.2
  • 220
    • 0034825091 scopus 로고    scopus 로고
    • Supervised versus unsupervised binary-learning by feedforward neural networks
    • DOI 10.1023/A:1007660820062
    • N. Japkowicz Supervised versus unsupervised binary-learning by feedforward neural networks Mach. Learn. 42 1 2001 97 122 (Pubitemid 32872400)
    • (2001) Machine Learning , vol.42 , Issue.1-2 , pp. 97-122
    • Japkowicz, N.1
  • 221
    • 33847410597 scopus 로고    scopus 로고
    • One-class document classification via Neural Networks
    • DOI 10.1016/j.neucom.2006.05.013, PII S092523120600261X, Advances in Computational Intelligence and Learning 14th European Symposium on Artificial Neural Networks 2006
    • L. Manevitz, and M. Yousef One-class document classification via neural networks Neurocomputing 70 7 2007 1466 1481 (Pubitemid 46336763)
    • (2007) Neurocomputing , vol.70 , Issue.7-9 , pp. 1466-1481
    • Manevitz, L.1    Yousef, M.2
  • 224
    • 0036061014 scopus 로고    scopus 로고
    • Fault-diagnosis using neural networks with ellipsoidal basis functions
    • IEEE
    • S. Jakubek, T. Strasser, Fault-diagnosis using neural networks with ellipsoidal basis functions, in: Proceedings of the American Control Conference, vol. 5, IEEE, 2002, pp. 3846-3851.
    • (2002) Proceedings of the American Control Conference , vol.5 , pp. 3846-3851
    • Jakubek, S.1    Strasser, T.2
  • 225
    • 0036501528 scopus 로고    scopus 로고
    • Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where 'unknown' faults may occur
    • DOI 10.1016/S0167-8655(01)00133-7, PII S0167865501001337
    • Y. Li, M. Pont, and N. Barrie Jones Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where unknown faults may occur Pattern Recognit. Lett. 23 5 2002 569 577 (Pubitemid 34262287)
    • (2002) Pattern Recognition Letters , vol.23 , Issue.5 , pp. 569-577
    • Li, Y.1    Pont, M.J.2    Barrie Jones, N.3
  • 226
    • 35248885111 scopus 로고    scopus 로고
    • A self-organizing neural network for detecting novelties
    • DOI 10.1145/1244002.1244110, Proceedings of the 2007 ACM Symposium on Applied Computing
    • M.K. Albertini, R.F. de Mello, A self-organizing neural network for detecting novelties, in: Proceedings of the 2007 ACM Symposium on Applied Computing, SAC '07, ACM, New York, NY, USA, 2007, pp. 462-466. (Pubitemid 47568335)
    • (2007) Proceedings of the ACM Symposium on Applied Computing , pp. 462-466
    • Albertini, M.K.1    De Mello, R.F.2
  • 227
    • 69049086736 scopus 로고    scopus 로고
    • Time series clustering for anomaly detection using competitive neural networks
    • J. Príncipe, R. Miikkulainen, Springer, Berlin Heidelberg
    • G. Barreto, and L. Aguayo Time series clustering for anomaly detection using competitive neural networks J. Príncipe, R. Miikkulainen, Advances in Self-Organizing Maps, Lecture Notes in Computer Science vol. 5629 2009 Springer, Berlin Heidelberg 28 36
    • (2009) Advances in Self-Organizing Maps, Lecture Notes in Computer Science , vol.5629 VOL. , pp. 28-36
    • Barreto, G.1    Aguayo, L.2
  • 228
    • 0037382210 scopus 로고    scopus 로고
    • On-line pattern analysis by evolving self-organizing maps
    • DOI 10.1016/S0925-2312(02)00599-4, PII S0925231202005994
    • D. Deng, and N. Kasabov On-line pattern analysis by evolving self-organizing maps Neurocomputing 51 2003 87 103 (Pubitemid 36367227)
    • (2003) Neurocomputing , vol.51 , pp. 87-103
    • Deng, D.1    Kasabov, N.2
  • 230
    • 37249061630 scopus 로고    scopus 로고
    • Ligand-based virtual screening by novelty detection with self-organizing maps
    • DOI 10.1021/ci700040r
    • D. Hristozov, T. Oprea, and J. Gasteiger Ligand-based virtual screening by novelty detection with self-organizing maps J. Chem. Inf. Model. 47 6 2007 2044 2062 (Pubitemid 350275072)
    • (2007) Journal of Chemical Information and Modeling , vol.47 , Issue.6 , pp. 2044-2062
    • Hristozov, D.1    Oprea, T.I.2    Gasteiger, J.3
  • 232
    • 0036789790 scopus 로고    scopus 로고
    • A self-organising network that grows when required
    • S. Marsland, J. Shapiro, and U. Nehmzow A self-organising network that grows when required Neural Netw. 15 8-9 2002 1041 1058
    • (2002) Neural Netw. , vol.15 , Issue.89 , pp. 1041-1058
    • Marsland, S.1    Shapiro, J.2    Nehmzow, U.3
  • 233
    • 16644365029 scopus 로고    scopus 로고
    • On-line novelty detection for autonomous mobile robots
    • DOI 10.1016/j.robot.2004.10.006, PII S0921889004002167
    • S. Marsland, U. Nehmzow, and J. Shapiro On-line novelty detection for autonomous mobile robots Robot. Auton. Syst. 51 2 2005 191 206 (Pubitemid 40480107)
    • (2005) Robotics and Autonomous Systems , vol.51 , Issue.2-3 , pp. 191-206
    • Marsland, S.1    Nehmzow, U.2    Shapiro, J.3
  • 235
    • 78049451757 scopus 로고    scopus 로고
    • An online adaptive condition-based maintenance method for mechanical systems
    • F. Wu, T. Wang, and J. Lee An online adaptive condition-based maintenance method for mechanical systems Mech. Syst. Signal Process. 24 8 2010 2985 2995
    • (2010) Mech. Syst. Signal Process. , vol.24 , Issue.8 , pp. 2985-2995
    • Wu, F.1    Wang, T.2    Lee, J.3
  • 237
    • 84863381941 scopus 로고    scopus 로고
    • Detecting anomalous insiders in collaborative information systems
    • Y. Chen, S. Nyemba, and B. Malin Detecting anomalous insiders in collaborative information systems IEEE Trans. Dependable Secur. Comput. 9 3 2012 332 344
    • (2012) IEEE Trans. Dependable Secur. Comput. , vol.9 , Issue.3 , pp. 332-344
    • Chen, Y.1    Nyemba, S.2    Malin, B.3
  • 239
    • 33750522220 scopus 로고    scopus 로고
    • Kernel PCA for novelty detection
    • DOI 10.1016/j.patcog.2006.07.009, PII S0031320306003414
    • H. Hoffmann Kernel PCA for novelty detection Pattern Recognit. 40 3 2007 863 874 (Pubitemid 44667761)
    • (2007) Pattern Recognition , vol.40 , Issue.3 , pp. 863-874
    • Hoffmann, H.1
  • 240
    • 33847290520 scopus 로고    scopus 로고
    • Mining anomalies using traffic feature distributions
    • DOI 10.1145/1090191.1080118
    • A. Lakhina, M. Crovella, and C. Diot Mining anomalies using traffic feature distributions ACM SIGCOMM Comput. Commun. Rev. 35 4 2005 217 228 (Pubitemid 46323506)
    • (2005) Computer Communication Review , vol.35 , Issue.4 , pp. 217-228
    • Lakhina, A.1    Crovella, M.2    Diot, C.3
  • 241
    • 59449095425 scopus 로고    scopus 로고
    • Incremental data-driven learning of a novelty detection model for one-class classification with application to high-dimensional noisy data
    • R. Kassab, and F. Alexandre Incremental data-driven learning of a novelty detection model for one-class classification with application to high-dimensional noisy data Mach. Learn. 74 2 2009 191 234
    • (2009) Mach. Learn. , vol.74 , Issue.2 , pp. 191-234
    • Kassab, R.1    Alexandre, F.2
  • 242
    • 79952320406 scopus 로고    scopus 로고
    • Feature extraction for novelty detection as applied to fault detection in machinery
    • J. McBain, and M. Timusk Feature extraction for novelty detection as applied to fault detection in machinery Pattern Recognit. Lett. 32 7 2011 1054 1061
    • (2011) Pattern Recognit. Lett. , vol.32 , Issue.7 , pp. 1054-1061
    • McBain, J.1    Timusk, M.2
  • 243
    • 33947132163 scopus 로고    scopus 로고
    • On-line novelty detection by recursive dynamic principal component analysis and gas sensor arrays under drift conditions
    • A. Perera, N. Papamichail, N. Bârsan, U. Weimar, and S. Marco On-line novelty detection by recursive dynamic principal component analysis and gas sensor arrays under drift conditions IEEE Sens. J. 6 3 2006 770 783
    • (2006) IEEE Sens. J. , vol.6 , Issue.3 , pp. 770-783
    • Perera, A.1    Papamichail, N.2    Bârsan, N.3    Weimar, U.4    Marco, S.5
  • 246
    • 0043166339 scopus 로고    scopus 로고
    • Anomaly detection in IP networks
    • M. Thottan, and C. Ji Anomaly detection in IP networks IEEE Trans. Signal Process. 51 8 2003 2191 2204
    • (2003) IEEE Trans. Signal Process. , vol.51 , Issue.8 , pp. 2191-2204
    • Thottan, M.1    Ji, C.2
  • 247
    • 77953782136 scopus 로고    scopus 로고
    • Novelty detection in projected spaces for structural health monitoring
    • J. Toivola, M. Prada, and J. Hollmén Novelty detection in projected spaces for structural health monitoring Adv. Intell. Data Anal. IX 6065 2010 208 219
    • (2010) Adv. Intell. Data Anal. IX , vol.6065 , pp. 208-219
    • Toivola, J.1    Prada, M.2    Hollmén, J.3
  • 248
    • 84866013812 scopus 로고    scopus 로고
    • L1 norm based KPCA for novelty detection
    • Y. Xiao, H. Wang, W. Xu, and J. Zhou L1 norm based KPCA for novelty detection Pattern Recognit. 46 1 2013 389 396
    • (2013) Pattern Recognit. , vol.46 , Issue.1 , pp. 389-396
    • Xiao, Y.1    Wang, H.2    Xu, W.3    Zhou, J.4
  • 249
    • 40249093592 scopus 로고    scopus 로고
    • Evolving a dynamic predictive coding mechanism for novelty detection
    • S. Haggett, D. Chu, and I. Marshall Evolving a dynamic predictive coding mechanism for novelty detection Knowl. Based Syst. 21 3 2008 217 224
    • (2008) Knowl. Based Syst. , vol.21 , Issue.3 , pp. 217-224
    • Haggett, S.1    Chu, D.2    Marshall, I.3
  • 250
    • 21844470231 scopus 로고    scopus 로고
    • Dynamic predictive coding by the retina
    • DOI 10.1038/nature03689
    • T. Hosoya, S. Baccus, and M. Meister Dynamic predictive coding by the retina Nature 436 7047 2005 71 77 (Pubitemid 40966187)
    • (2005) Nature , vol.436 , Issue.7047 , pp. 71-77
    • Hosoya, T.1    Baccus, S.A.2    Meister, M.3
  • 251
    • 0025489075 scopus 로고
    • The self-organizing map
    • T. Kohonen The self-organizing map Proc. IEEE 78 9 1990 1464 1480
    • (1990) Proc. IEEE , vol.78 , Issue.9 , pp. 1464-1480
    • Kohonen, T.1
  • 252
    • 0142095451 scopus 로고    scopus 로고
    • NSOM a real-time network-based intrusion detection system using self-organizing maps
    • K. Labib, and R. Vemuri NSOM a real-time network-based intrusion detection system using self-organizing maps Netw. Secur. 2002 1 6
    • (2002) Netw. Secur. , pp. 1-6
    • Labib, K.1    Vemuri, R.2
  • 253
    • 0034186912 scopus 로고    scopus 로고
    • Dynamic self-organizing maps with controlled growth for knowledge discovery
    • D. Alahakoon, S. Halgamuge, and B. Srinivasan Dynamic self-organizing maps with controlled growth for knowledge discovery IEEE Trans. Neural Netw. 11 3 2000 601 614
    • (2000) IEEE Trans. Neural Netw. , vol.11 , Issue.3 , pp. 601-614
    • Alahakoon, D.1    Halgamuge, S.2    Srinivasan, B.3
  • 255
    • 0028748949 scopus 로고
    • Growing cell structures - A self-organizing network for unsupervised and supervised learning
    • B. Fritzke Growing cell structures - a self-organizing network for unsupervised and supervised learning Neural Netw. 7 9 1994 1441 1460
    • (1994) Neural Netw. , vol.7 , Issue.9 , pp. 1441-1460
    • Fritzke, B.1
  • 256
    • 85135470835 scopus 로고
    • A growing neural gas network learns topologies
    • B. Fritzke A growing neural gas network learns topologies Adv. Neural Inf. Process. Syst. 7 1995 625 632
    • (1995) Adv. Neural Inf. Process. Syst. , vol.7 , pp. 625-632
    • Fritzke, B.1
  • 258
    • 32344449062 scopus 로고    scopus 로고
    • An approach to spacecraft anomaly detection problem using Kernel Feature Space
    • DOI 10.1145/1081870.1081917, KDD-2005 - Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    • R. Fujimaki, T. Yairi, K. Machida, An approach to spacecraft anomaly detection problem using kernel feature space, in: Proceedings of the 11th ACM International Conference on Knowledge Discovery in Data Mining (SIGKDD), ACM, 2005, pp. 401-410. (Pubitemid 43218302)
    • (2005) Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , pp. 401-410
    • Fujimaki, R.1    Yairi, T.2    Machida, K.3
  • 259
    • 0347243182 scopus 로고    scopus 로고
    • Nonlinear Component Analysis as a Kernel Eigenvalue Problem
    • B. Schölkopf, A. Smola, and K. Müller Nonlinear component analysis as a kernel eigenvalue problem Neural Comput. 10 5 1998 1299 1319 (Pubitemid 128463674)
    • (1998) Neural Computation , vol.10 , Issue.5 , pp. 1299-1319
    • Scholkopf, B.1    Smola, A.2    Muller, K.-R.3
  • 260
    • 48049103479 scopus 로고    scopus 로고
    • Principal component analysis based on l1-norm maximization
    • N. Kwak Principal component analysis based on l1-norm maximization IEEE Trans. Pattern Anal. Mach. Intell. 30 9 2008 1672 1680
    • (2008) IEEE Trans. Pattern Anal. Mach. Intell. , vol.30 , Issue.9 , pp. 1672-1680
    • Kwak, N.1
  • 267
    • 0033220728 scopus 로고    scopus 로고
    • Support vector domain description
    • DOI 10.1016/S0167-8655(99)00087-2
    • D. Tax, and R. Duin Support vector domain description Pattern Recognit. Lett. 20 11 1999 1191 1199 (Pubitemid 32261897)
    • (1999) Pattern Recognition Letters , vol.20 , Issue.11-13 , pp. 1191-1199
    • Tax, D.M.J.1    Duin, R.P.W.2
  • 268
    • 0036881508 scopus 로고    scopus 로고
    • Robust support vector machine with bullet hole image classification
    • Q. Song, W. Hu, and W. Xie Robust support vector machine with bullet hole image classification IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 32 4 2002 440 448
    • (2002) IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. , vol.32 , Issue.4 , pp. 440-448
    • Song, Q.1    Hu, W.2    Xie, W.3
  • 271
    • 85096855936 scopus 로고    scopus 로고
    • One-class SVMs for document classification
    • L. Manevitz, and M. Yousef One-class SVMs for document classification J. Mach. Learn. Res. 2 2002 139 154
    • (2002) J. Mach. Learn. Res. , vol.2 , pp. 139-154
    • Manevitz, L.1    Yousef, M.2
  • 274
    • 79957956807 scopus 로고    scopus 로고
    • Multiple distribution data description learning algorithm for novelty detection
    • T. Le, D. Tran, W. Ma, and D. Sharma Multiple distribution data description learning algorithm for novelty detection Adv. Knowl. Discov. Data Min. 6635 2011 246 257
    • (2011) Adv. Knowl. Discov. Data Min. , vol.6635 , pp. 246-257
    • Le, T.1    Tran, D.2    Ma, W.3    Sharma, D.4
  • 275
    • 77955514045 scopus 로고    scopus 로고
    • Fast support vector data descriptions for novelty detection
    • Y.-H. Liu, Y.-C. Liu, and Y.-J. Chen Fast support vector data descriptions for novelty detection IEEE Trans. Neural Netw. 21 8 2010 1296 1313
    • (2010) IEEE Trans. Neural Netw. , vol.21 , Issue.8 , pp. 1296-1313
    • Liu, Y.-H.1    Liu, Y.-C.2    Chen, Y.-J.3
  • 276
    • 79151475707 scopus 로고    scopus 로고
    • High-speed inline defect detection for TFT-LCD array process using a novel support vector data description
    • Y.-H. Liu, Y.-C. Liu, and Y.-Z. Chen High-speed inline defect detection for TFT-LCD array process using a novel support vector data description Exp. Syst. Appl. 38 5 2011 6222 6231
    • (2011) Exp. Syst. Appl. , vol.38 , Issue.5 , pp. 6222-6231
    • Liu, Y.-H.1    Liu, Y.-C.2    Chen, Y.-Z.3
  • 277
    • 84867736801 scopus 로고    scopus 로고
    • Efficient support vector data descriptions for novelty detection
    • X. Peng, and D. Xu Efficient support vector data descriptions for novelty detection Neural Comput. Appl. 21 8 2012 2023 2032
    • (2012) Neural Comput. Appl. , vol.21 , Issue.8 , pp. 2023-2032
    • Peng, X.1    Xu, D.2
  • 278
    • 70349915779 scopus 로고    scopus 로고
    • A small sphere and large margin approach for novelty detection using training data with outliers
    • M. Wu, and J. Ye A small sphere and large margin approach for novelty detection using training data with outliers IEEE Trans. Pattern Anal. Mach. Intell. 31 11 2009 2088 2092
    • (2009) IEEE Trans. Pattern Anal. Mach. Intell. , vol.31 , Issue.11 , pp. 2088-2092
    • Wu, M.1    Ye, J.2
  • 281
    • 34748909393 scopus 로고    scopus 로고
    • Combined support vector novelty detection for multi-channel combustion data
    • DOI 10.1109/ICNSC.2007.372828, 4239041, 2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07
    • L. Clifton, H. Yin, D. Clifton, Y. Zhang, Combined support vector novelty detection for multi-channel combustion data, in: Proceedings of the IEEE International Conference on Networking, Sensing and Control, IEEE, 2007, pp. 495-500. (Pubitemid 47468840)
    • (2007) 2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07 , pp. 495-500
    • Clifton, L.A.1    Yin, H.2    Clifton, D.A.3    Zhang, Y.4
  • 288
    • 33745815656 scopus 로고    scopus 로고
    • Application of LVQ to novelty detection using outlier training data
    • DOI 10.1016/j.patrec.2006.02.019, PII S0167865506000754
    • H. Lee, and S. Cho Application of LVQ to novelty detection using outlier training data Pattern Recognit. Lett. 27 13 2006 1572 1579 (Pubitemid 44037113)
    • (2006) Pattern Recognition Letters , vol.27 , Issue.13 , pp. 1572-1579
    • Lee, H.-j.1    Cho, S.2
  • 292
    • 57649150671 scopus 로고    scopus 로고
    • Parameter optimization of kernel-based one-class classifier on imbalance learning
    • L. Zhuang, and H. Dai Parameter optimization of kernel-based one-class classifier on imbalance learning J. Comput. 1 7 2006 32 40
    • (2006) J. Comput. , vol.1 , Issue.7 , pp. 32-40
    • Zhuang, L.1    Dai, H.2
  • 295
    • 33645717587 scopus 로고    scopus 로고
    • Kernel fisher discriminants for outlier detection
    • DOI 10.1162/089976606775774679
    • V. Roth Kernel fisher discriminants for outlier detection Neural Comput. 18 4 2006 942 960 (Pubitemid 43543827)
    • (2006) Neural Computation , vol.18 , Issue.4 , pp. 942-960
    • Roth, V.1
  • 296
    • 77953098563 scopus 로고    scopus 로고
    • Anomaly detection through a Bayesian support vector machine
    • V. Sotiris, P. Tse, and M. Pecht Anomaly detection through a Bayesian support vector machine IEEE Trans. Reliab. 59 2 2010 277 286
    • (2010) IEEE Trans. Reliab. , vol.59 , Issue.2 , pp. 277-286
    • Sotiris, V.1    Tse, P.2    Pecht, M.3
  • 299
    • 27144543595 scopus 로고    scopus 로고
    • An optimization model for outlier detection in categorical data
    • Advances in Intelligent Computing: International Conference on Intelligent Computing, ICIC 2005. Proceedings
    • Z. He, S. Deng, and X. Xu An optimization model for outlier detection in categorical data Adv. Intell. Comput. 3644 2005 400 409 (Pubitemid 41491080)
    • (2005) Lecture Notes in Computer Science , vol.3644 , Issue.PART I , pp. 400-409
    • He, Z.1    Deng, S.2    Xu, X.3
  • 303
    • 33845271242 scopus 로고    scopus 로고
    • Finding the most unusual time series subsequence: Algorithms and applications
    • DOI 10.1007/s10115-006-0034-6
    • E. Keogh, J. Lin, S. Lee, and H. Herle Finding the most unusual time series subsequence algorithms and applications Knowl. Inf. Syst. 11 1 2007 1 27 (Pubitemid 44857541)
    • (2007) Knowledge and Information Systems , vol.11 , Issue.1 , pp. 1-27
    • Keogh, E.1    Lin, J.2    Lee, S.-H.3    Van Herle, H.4
  • 304
    • 27544465147 scopus 로고    scopus 로고
    • Approximations to magic: Finding unusual medical time series
    • Proceedings - 18th IEEE Symposium on Computer-Based Medical Systems
    • J. Lin, E. Keogh, A. Fu, H. Van Herle, Approximations to magic: finding unusual medical time series, in: Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems, IEEE, 2005, pp. 329-334. (Pubitemid 41542915)
    • (2005) Proceedings - IEEE Symposium on Computer-Based Medical Systems , pp. 329-334
    • Lin, J.1    Keogh, E.2    Fu, A.3    Van Herle, H.4
  • 307
    • 70449717021 scopus 로고    scopus 로고
    • Information theoretic novelty detection
    • M. Filippone, and G. Sanguinetti Information theoretic novelty detection Pattern Recognit. 43 3 2010 805 814
    • (2010) Pattern Recognit. , vol.43 , Issue.3 , pp. 805-814
    • Filippone, M.1    Sanguinetti, G.2
  • 308
    • 79951607505 scopus 로고    scopus 로고
    • A perturbative approach to novelty detection in autoregressive models
    • M. Filippone, and G. Sanguinetti A perturbative approach to novelty detection in autoregressive models IEEE Trans. Signal Process. 59 3 2011 1027 1036
    • (2011) IEEE Trans. Signal Process. , vol.59 , Issue.3 , pp. 1027-1036
    • Filippone, M.1    Sanguinetti, G.2
  • 310
    • 67349174184 scopus 로고    scopus 로고
    • Bayesian surprise attracts human attention
    • L. Itti, and P. Baldi Bayesian surprise attracts human attention Vis. Res. 49 10 2009 1295 1306
    • (2009) Vis. Res. , vol.49 , Issue.10 , pp. 1295-1306
    • Itti, L.1    Baldi, P.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.