One-class classification: Taxonomy of study and review of techniques

Knowledge Engineering Review

Volumn 29, Issue 3, 2014, Pages 345-374

  Khan, Shehroz S ^a   Madden, Michael G ^b  

^a UNIVERSITY OF WATERLOO   (Canada)

^b NATIONAL UNIVERSITY OF IRELAND   (Ireland)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; RESEARCH;

CLASS BOUNDARY; CLASSIFICATION MODELS; CONCEPT LEARNING; LITERATURE REVIEWS; ONE-CLASS CLASSIFICATION; RESEARCH PROBLEMS; TRAINING DATA;

TAXONOMIES;

EID: 84900011520     PISSN: 02698889     EISSN: 14698005     Source Type: Journal    
DOI: 10.1017/S026988891300043X     Document Type: Review

References (185)

1. Alashwal, H. T., Deris, S. & Othman, R. M. 2006. One-class support vector machines for protein-protein interactions prediction. International Journal of Biomedical Sciences 1(2), 120-127
2. Bache, K. & Lichman, M. 2013. UCI machine learning repository. Accessed on July 2013 from http:// archive.ics.uci.edu/ml
3. Ban, T. & Abe, S. 2006. Implementing multi-class classifiers by one-class classification methods. In International Joint Conference on Neural Networks, 327-332
4. Bartkowiak, A. M. 2011. Anomaly, novelty, one-class classification: A comprehensive introduction. International Journal of Computer Information Systems and Industrial Management Applications 3, 61-71
5. Bauer, E. & Kohavi, R. 1999. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning 36, 105-139
6. Bergamini, C., Koerich, A. L. & Sabourin, R. 2009. Combining different biometric traits with one-class classification. Signal Processing 89(11), 2117-2127
7. Bergamini, C., Oliveira, L. S., Koerich, A. L. & Sabourin, R. 2008. Fusion of biometric systems using one-class classification. In Proceedings of IEEE International Joint Conference on Neural Networks, Hong Kong, 1308-1313
8. Bicego, M., Grosso, E. & Tistarelli, M. 2005. Face authentication using one-class support vector machines. In Advances in Biometric Person Authentication: International Workshop on Biometric Recognition Systems, in Conjunction with International Conference On Computer Vision. Lecture Notes in Computer Science 3781, 15-22
9. Bishop, C. 1994. Novelty detection and neural network validation. In IEEE Proceedings on Vision, Image and Signal Processing, 141 of 4, 217-222
10. Blanchard, G., Lee, G. & Scott, C. 2010. Semi-supervised novelty detection. Journal of Machine Learning Research 11, 2973-3009
11. Blum, A. & Mitchell, T. 1998. Combining labeled and unlabeled data with co-Training. In Proceedings of 11th Annual Conference on Computation Learning Theory, Bartlett, P. L. & Mansour, Y. (eds). ACM Press, 92-100
12. Bosco, G. L. & Pinello, L. 2009. A fuzzy one class classifier for multi layer model. Fuzzy Logic and Application, Lecture Notes in Computer Science 5571, 124-131
13. Breiman, L. 1996. Bagging predictors. Machine Learning 24, 123-140
14. Brew, A., Grimaldi, M. & Cunningham, P. 2007. An evaluation of one-class classification techniques for speaker verification. Artificial Intelligence Review 27(4), 295
15. Cabral, G. G., Oliveira, A. L. I. & Cahu, C. B. G. 2007. A novel method for one-class classification based on the nearest neighbor data description and structural risk minimization. In Proceedings of International Joint Conference on Neural Networks, Orlando, FL, 1976-1981
16. Cabral, G. G., Oliveira, A. L. I. & Cahu, C. B. G. 2009. Combining nearest neighbor data description and structural risk minimization for one-class classification. Neural Computing and Applications 18(2), 175-183
17. Calvo, B. 2008. Positive unlabelled learning with applications in computational biology. Phd thesis, University of the Basque Country
18. Calvo, B., Larrañaga, P. & Lozano, J. A. 2007. Learning bayesian classifiers from positive and unlabeled examples. Pattern Recognition Letters 28(16), 2375-2384
19. Campbell, C. & Bennett, K. P. 2001. A linear programming approach to novelty detection. In Advances in Neural Information Processing, Leen, T. K., Dietterich, T. D. & Tresp, V. (eds). MIT Press, 14. Cambridge, M.A.
20. Cerulo, L., Elkan, C. & Ceccarelli, M. 2010. Learning gene regulatory networks from only positive and unlabeled data. BMC Bioinformatics 11, 228
21. Chandola, V., Banerjee, A. & Kumar, V. 2009. Anomaly detection-A survey. ACM Computing Surveys 41(3), 15:1-15:58
22. Chen, H. F., Yao, D. Z., Becker, S., Zhou, Y., Zeng, M. & Chen, L. 2002. A new method for fMRI data processing: Neighborhood independent component correlation algorithm and its preliminary application. Science in China Series F 45(5), 373-382
23. Chen, L. & Pu, P. 2004. Survey of Preference Elicitation Methods. Technical report, EPFL
24. Chen, Y., Zhou, X. & Huang, T. S. 2001. One-class SVM for learning in image retrieval. In Proceedings of IEEE International Conference on Image Processing, Greece
25. Cheplygina, V. & Tax, D. M. J. 2011. Pruned random subspace method for one-class classifiers. In 10th International Workshop, Sansone, C., Kittler, J. & Roli, F. (eds). MCS 2011, Lecture Notes in Computer Science 6713, 96-105. Springer
26. Choi, Y. S. & Kim, K. J. 2004. Video summarization using fuzzy one-class support vector machine. In Lecture Notes in Computer Science, Laganà, A., Gavrilova, M. L., Kumar, V., Mun, Y., Tan, C. J. K. & Gervasi, O. (eds). 3043, 49-56. Springer-Verlag
27. Cohen, G., Hilario, M., Sax, H., Hugonnet, S., Pellegrini, C. & Geissbuhler, A. 2004. An application of oneclass support vector machines to nosocomial infection detection. In Proceedings of Medical Informatics
28. Cohen, I., Sebe, N., Gozman, F. G., Cirelo, M. C. & Huang, T. S. 2003. Learning bayesian network classifiers for facial expression recognition both labeled and unlabeled data. In Proceedings of 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003, 1. IEEE, 595-601
29. Cun, Y. L., Boser, B., Denker, J.S., Henderson, D., Howard, R. E., Hubbard, W. & Jackel, L. D. 1989. Backpropagation applied to handwritten zip code recognition. Neural Computation 1, 541-551
30. Datta, P. 1997. Characteristic Concept Representations. PhD thesis, University of California Irvine
31. Denis, F. 1998. PAC learning from positive statistical queries. In Proceedings of the 9th International Conference on Algorithmic Learning Theory, Richter, M. M., Smith, C. H., Wiehagen, R. & Zeugmann, T. (eds). Springer-Verlag, 112-126
32. Denis, F., Gilleron, R. & Tommasi, M. 2002. Text classification from positive and unlabeled examples. In Proceedings of 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Annecy, France
33. Denis, F., Laurent, A., Gilleron, R. & Tommasi, M. 2003. Text classification and co training from positive and unlabeled examples. In Proceedings of the ICML Workshop: The Continuum from Labeled Data to Unlabeled Data in Machine Learning and Data Mining, 80-87, Washington DC, USA
34. Désir, C., Bernard, S., Petitjean, C. & Heutte, L. 2012. A random forest based approach for one class classification in medical imaging. In Machine Learning in Medical Imaging, Wang, F., Shen, D., Yan, P. & Suzuki, K. (eds). Lecture Notes in Computer Science 7588, 250-257. Springer
35. De Comité, F., Denis, F., Gilleron, R. & Letouzey, F. 1999. Positive and unlabeled examples help learning. In Proceedings of the 10th International Conference on Algorithmic Learning Theory, Watanabe, O. & Yokomori, T. (eds). Springer-Verlag, 219-230
36. De Haro-Garca, A., Garca-Pedrajas, N., Romero del Castillo, J. A. & Garca-Pedrajas, M. D. 2009. One-class methods for separating plant/pathogen sequences. In VI Congreso Espaol sobre Metaheursticas, Algoritmos Evolutivos y Bioinspirados, Malaga, Spain
37. De Ridder, D., Tax, D. M. J. & Duin, R. P. W. 1998. An experimental comparison of one-class classification methods. In Proceedings of the 4th Annual Conference of the Advanced School for Computing and Imaging, Delft
38. Dietterich, T. G. 2000. An experimental comparison of three methods for constructing ensembles of decision trees, bagging, boosting and randomization. Machine Learning 40(2), 139-157
39. Domingos, P. & Hulten, G. 2000. Mining high-speed data streams. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Ramakrishnan, R., Stolfo, S. J., Bayardo, R. J. & Parsa, I. (eds). ACM, 71-80
40. Elkan, C. & Noto, K. 2008. Learning classifiers from only positive and unlabeled data. In Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Li, Y., Liu, B. & Sarawagi, S. (eds). ACM, 213-220
41. El-Yaniv, R. & Nisenson, M. 2006. Optimal single-class classification strategies-google scholar. In Proceedings of the 2006 NIPS Conference, Schölkopf, B., Platt, J. C. & Hoffman, T. (eds). 19. MIT Press, 377-384
42. Ercil, A. & Buke, B. 2002. One class classification using implicit polynomial surface fitting. In Proceedings of the 16th International Conference on Pattern Recognition, Quebec, Canada, 2, 152-155
43. Evangelista, P. F., Bonnisone, P., Embrechts, M. J. & Szymanski, B. K. 2005. Fuzzy ROC curves for the 1 class SVM: Application to intrusion detection. In Application to Intrusion Detection, 13th European Symposium on Artificial Neural Networks, Burges
44. Ferreira De Carvalho, A. C. P. L. 2005. Combining one-class classifiers for robust novelty detection in gene expression data. In Brazilian Symposium on Bioinformatics, 54-64
45. Gardner, B., Krieger, A. M., Vachtsevanos, G. & Litt, B. 2006. One-class novelty detection for seizure analysis from intracranial EEG. Journal of Machine Learning Research 7, 1025-1044
46. Gesù, V. D. & Bosco, G. L. 2007. Combining one class fuzzy KNN's. Applications of Fuzzy Sets Theory Lecture Notes in Computer Science 4578, 152-160
47. Gesú, V. D., Bosco, G. L. & Pinello, L. 2008. A one class classifier for signal identification: A biological case study. In Proceedings of the 12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, Lovrek, I., Howlett, R. J. & Jain, L. C. (eds). 5179. Springer, 747-754
48. Gesù, V. D., Bosco, G. L., Pinello, L., Yuan, G. C. & Corona, D. F. V. 2009. A multi-layer method to study genome-scale positions of nucleosomes. Genomics 930(2), 140-145
49. Giacinto, G., Perdisci, R. & Roli, F. 2005. Network intrusion detection by combining one-class classifiers. In Image Analysis and Processing-ICIAP 2005, Roli, F. & Vitulano, S. (eds). Lecture Notes in Computer Science 3617, 58-65. Springer
50. Glavin, F. G. & Madden, M. G. 2009. Analysis of the effect of unexpected outliers in the classification of spectroscopy data. In Artificial Intelligence and Cognitive Science 2009, Dublin
51. Gondra, I., Heisterkamp, D. R. & Peng, J. 2004. Improving image retrieval performance by inter-query learning with one-class support vector machines. Neural Computation and Applications 13, 130-139
52. Hao, P. Y. 2008. Fuzzy one-class support vector machines. Fuzzy Sets and Systems 159, 2317-2336
53. Hao, L., Wen, Z. J., Sheng, C. Q., Rong, C. J. & Ping, Z. 2010. Identification of egg freshness using near infrared spectroscopy and one class support vector machine algorithm. Spectroscopy and Spectral Analysis 30(4), 929-932
54. Hardoon, D. R. & Manevitz, L. M. 2005a. fMRI analysis via one-class machine learning techniques. In Proceedings of 19th International Joint Conference on Aritifical Intelligence, Edinburgh, UK, 1604-1606
55. Hardoon, D. R. & Manevitz, L. M. 2005b. One-class machine learning approach for fMRI analysis. In In Postgraduate Research Conference in Electronics, Photonics, Communications and Networks, and Computer Science, Lancaster
56. Hempstalk, K., Frank, E. & Witten, I. H. 2008. One-class classification by combining density and class probability estimation. In Proceedings of the 12th European Conference on Principles and Practice of Knowledge Discovery in Databases and 19th European Conference on Machine Learning, Berlin, 505-519
57. He, J., Zhang, Y., Li, X. & Wang, Y. 2010. Naive bayes classifier for positive unlabeled learning with uncertainty. In Proceedings of the 10th SIAM International Conference on Data Mining, USA, 361-372
58. Howley, T. & Madden, M. G. 2006. An evolutionary approach to automatic kernel construction. In Proceedings of ICANN 2006, Lecture Notes in Computer Science 4132, 417-426
59. Howley, T. 2007. Kernel methods for machine learning with applications to the analysis of raman spectra. Phd thesis, National University of Ireland Galway
60. Ho, T. K. 1998. The random subspace method for constructing decision forests. IEEE Trans. Pattern Analysis and Machine Intelligence, 200(8): 832-844
61. Japkowicz, N. 1999. Concept-Learning in the absence of counterexamples: An autoassociation-based approach to classification. PhD thesis, New Brunswick Rutgers, The State University of New Jersey
62. Juszczak, P. 2006. Learning to Recognise. A study on one-class classification and active learning. PhD thesis, Delft University of Technology
63. Juszczak, P. & Duin, R. P. W. 2004. Combining one-class classifiers to classify missing data. In Proceedings of the 5th International Workshop MCS, Roli, F., Kittler, J. & Windeatt, T. (eds). Springer-Verlag, 3077, 92-101
64. Juszczak, P., Tax, D. M. J., Pȩkalska, E. & Duin, R. P. W. 2009. Minimum spanning tree based one-class classifier. Neurocomputing 72(7-9), 1859-1869
65. Kennedy, J. & Eberhart, R. 1995. Particle swarm optimization. In Proceedings IEEE International Conference on Neural Networks, Piscataway, NJ, 1942-1948
66. Kennedy, K., Mac Namee, B. & Delany, S. J. 2009. Credit scoring: Solving the low default portfolio problem using one-class classification. In Proceedings of the 20th Irish Conference on Artificial Intelligence and Cognitive Science, 168-177
67. Khan, S. S., Hoey, J. & Lizotte, D. 2012a. Bayesian multiple imputation approaches for one-class classification-springer. In Proceedings Advances in Artificial Intelligence, Kosseim, L. & Inkpen, D. (eds). Springer, Toronto, 7310, 331-336
68. Khan, S. S., Karg, M. E., Hoey, J. & Kulic, D. 2012b. Towards the detection of unusual temporal events during activities using HMMs. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Dey, A. K., Chu, H-H. & Hayes, G. R. (eds). UbiComp '12, ACM, 1075-1084. New York, NY, USA
69. Khan, S. S. 2010. Kernels for One-Class Nearest Neighbour Classification and Comparison of Chemical Spectral Data. Masters thesis, National University of Ireland Galway
70. Khan, S. S. & Madden, M. G. 2009. A survey of recent trends in one class classification. In Lecture Notes in Artificial Intelligence, Coyle, L. & Freyne, J. (eds). 6206, 181-190, Springer-Verlag
71. Kittiwachana, S., Ferreira, D. L. S., Lloyd, G. R., Fido, L. A., Thompson, D. R., Escott, R. E. A. & Brereton, R. G. 2010. One class classifiers for process monitoring illustrated by the application to online HPLC of a continuous process. Journal of Chemometrics 24(3-4), 96-110
72. Koppel, M. & Schler, J. 2004. Authorship verification as a one-class classification problem. In Proceedings of the 21st International Conference on Machine learning, Brodley, C. E. (ed.). ACMPress, 69. Alberta, Canada
73. Kowalczyk, A. & Raskutti, B. 2002. One class SVM for yeast regulation prediction. In ACM SIGKDD Explorations Newsletter, 4. ACM, 99-100
74. Kruengkrai, C. & Jaruskulchai, C. 2003. Using one-class SVMs for relevant sentence extraction. In International Symposium on Communications and Information Technologies, Thailand
75. Kuncheva, L. I. & Rodrguez, J. J. 2007. Classifier ensembles with a random linear oracle. IEEE Transactions on Knowledge and Data Engineering 19(4), 500-508
76. Lai, C., Tax, D. M. J., Duin, R. P. W., Pȩkalska, E. & Paclk, P. 2002. On combining one-class classifiers for image database retrieval. In Proceedings of the 3rd International Workshop on Multiple Classifier Systems, 212-221, Italy
77. Lee, W. & Liu, B. 2003. Learning with positive and unlabeled examples using weighted logistic regression. In Proceedings of the 20th International Conference on Machine Learning, Washington DC, USA
78. Letouzey, F., Denis, F. & Gilleron, R. 2000. Learning from positive and unlabeled examples. In Proceedings of 11th International Conference on Algorithmic Learning Theory, Sydney, Australia
79. Ling, C. X. & Sheng, V. S. 2010. Cost-sensitive learning. In Encyclopedia of Machine Learning, Sammut, C. & Webb, G. I. (eds). Springer, 231-235
80. Liu, B., Dai, Y., Li, X., Lee, W. S. & Yu, P. S. 2003. Building text classifiers using positive and unlabeled examples. In Proceedings of the 3rd IEEE International Conference on Data Mining, Florida, USA
81. Liu, B., Lee, W. S., Yu, P. S. & Li, X. 2002. Partially supervised classification of text documents. In Proceedings of the 19th International Conference on Machine Learning. Morgan Kaufmann Publishers Inc, Australia, 387-394
82. Liu, B., Xiao, Y., Cao, L. & Yu, P. S. 2011. One-class based uncertain data stream learning. In SIAM International Conference on Data Mining, 992-1003, Arizona, USA
83. Li, C. & Zhang, Y. 2008. Bagging one-class decision trees. In Proceedings of 5th International Conference on Fuzzy Systems and Knowledge Discovery, Shandong, 420-423
84. Li, C., Zhang, Y. & Li, X. 2009. OcVFDT: One-class very fast decision tree for one-class classification of data streams. In Proceedings of the 3rd International Workshop on Knowledge Discovery from Sensor Data, Omitaomu, O. A., Ganguly, A. R., Gama, J., Vatsavai, R. R., Chawla, N. V. & Gaber, M. M. (eds). SensorKDD '09, ACM, 79-86. New York, NY, USA
85. Li, K., Huang, H., Tian, S. & Xu, W. 2003. Improving one-class SVM for anomaly detection. In Proceedings of the 2nd International Conference on Machine Learning and Cybernetics, 5, 3077-3081
86. Li, W., Guo, Q. & Elkan, C. 2011. A positive and unlabeled learning algorithm for one-class classification of remote-sensing data. IEEE Transactions on Geoscience and Remote Sensing 49(2), 717-725
87. Li, X. & Liu, B. 2003. Learning to classify texts using positive and unlabeled data. In Proceedings of 18th International Joint Conference on Artificial Intelligence, 587-594, Mexico
88. Luenberger, D. G. 1984. Linear and Nonlinear Programming, 2nd ed. Addison-Wesley
89. Luo, J., Ding, L., Pan, Z., Ni, G. & Hu, G. 2007. Research on cost-sensitive learning in one-class anomaly detection algorithms. In Autonomic and Trusted Computing, Lecture Notes in Computer Science 4610, 259-268. Springer
90. Lyu, S. & Farid, H. 2004. Steganalysis using color wavelet statistics and one class support vector machines. In Proceedings of SPIE 5306, 35-45, San Jose, USA
91. Madden, M. G. & Howley, T. 2008. A machine learning application for classification of chemical spectra. In Proceedings of 28th SGAI International Conference, Cambridge, U.K.
92. Manevitz, L. M. & Yousef, M. 2000a. Document classification on neural networks using only positive examples. In Proceedings of 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 304-306, Athens, Greece
93. Manevitz, L. M. & Yousef, M. 2000b. Learning from positive data for document classification using neural networks. In Proceedings of 2nd Bar-Ilan Workshop on Knowledge Discovery and Learning, Israel
94. Manevitz, L. M. & Yousef, M. 2001. One-class SVMs for document classification. Journal of Machine Learning Research 2, 139-154
95. Markou, M. & Singh, S. 2003a. Novelty detection: A review-part 1: Statistical approaches. Signal Processing 83(12), 2481-2497
96. Markou, M. & Singh, S. 2003b. Novelty detection: A review-part 2: Neural networks based approaches. Signal Processing 83(12), 2499-2521
97. Mazhelis, O. 2006. One-class classifiers: A review and analysis of suitability in the context of mobilemasquerader detection. South African Computer Journal (SACJ), ARIMA & SACJ Joint Special Issue on Advances in End-User Data-mining Techniques 36, 29-48
98. Minter, T. C. 1975. Single-class classification. In Symposium on Machine Processing of Remotely Sensed Data. IEEE, 2A12-2A15
99. Moya, M. R., Koch, M. W. & Hostetler, L. D. 1993. One-class classifier networks for target recognition applications. In International Neural Network Society, 797-801, Portland, O.R.
100. Muggleton, S. 2001. Learning from the positive data. Machine Learning
101. Munroe, D. T. & Madden, M. G. 2005. Multi-class and single-class classification approaches to vehicle model recognition from images. In Proceedings of Irish Conference on Artificial Intelligence and Cognitive Science, Portstewart
102. Murshed, N., Bortolozzi, F. & Sabourin, R. 1996. Classification of cancerous cells based on the one-class problem approach. In SPIE Conference on Applications and Science of Artificial Neural Networks II, 2760, 487-494, Orlando, USA
103. Nanni, L. 2006. Experimental comparison of one-class classifiers for online signature verification. Neurocomputing 69, 869-873
104. Nguyen, B. V. 2002. An application of support vector machines to anomaly detection. Technical Report CS681, Ohio University
105. Nguyen, H., Abdesselam Bouzerdoum, Giang. & Son, L. Phung 2009. Learning pattern classification tasks with imbalanced data sets. In Pattern Recognition, Peng-Yeng Yin (ed.). InTech
106. Nguyen, M. N., Li, X. L. & Ng, S. K. 2011. Positive unlabeled learning for time series classification. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence, 16-22, Spain
107. Nigam, K., McCallum, A., Thrun, S. & Mitchell, T. 2000. Text classification from labeled and unlabeled documents using EM. Machine Learning 39(2/3), 103-134
108. Onoda, T., Murata, H. & Yamada, S. 2005. One class support vector machine based non-relevance feedback document retrieval. In International Joint Conference on Neural Networks 2005, Montreal, Canada
109. Pan, R., Zhou, Y., Cao, B., Liu, N. N., Lukose, R., Scholz, M. & Yang, Q. 2008. One-class collaborative filtering. In Proceedings of 8th IEEE International Conference on Data Mining, Italy
110. Pan, S., Zhang, Y., Li, X. & Wang, Y. 2010. Nearest neighbor algorithm for positive and unlabeled learning with uncertainty. Journal of Computer Science and Frontiers 4(9), 766-779
111. Pȩkalska, E., Skurichina, M. & Duin, R. P. W. 2004. Combining dissimilarity representations in one-class classifier problems. In Proceedings Fifth International Workshop MCS 2004, Springer, 3077, 122-133
112. Pȩkalska, E., Tax, D. M. J. & Duin, R. P. W. 2002. One-class LP classifiers for dissimilarity representations. In Advances in Neural Info. Processing Systems, Becker, S., Thrun, S. & Obermayer, K. (eds). 15. MIT Press, 761-768, British Columbia, Canada
113. Peng, T., Zuo, W. & He, F. 2006. Text classification from positive and unlabeled documents based on GA. In Proceedings of VECPAR'06, Brazil
114. Perdisci, R., Gu, G. & Lee, W. 2006. Using an ensemble of one-class SVM classifiers to harden payload-based anomaly detection systems. In Proceedings of the 16th International Conference on Data Mining. IEEE Computer Society, 488-498
115. Quinlan, J. M., Chalup, S. K. & Middleton, R. H. 2003. Application of SVMs for colour classification and collision detection with AIBO robots. Advances in Neural Information Processing Systems 16, 635-642
116. Quinlan, J. R. 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann
117. Rabaoui, A., Davy, M., Rossignol, S. & Ellouze, N. 2008. Using one-class SVMs and wavelets for audio surveillance systems. IEEE Trans. on Information Forensic and Security 3(4), 763-775
118. Rabaoui, A., Davy, M., Rossignol, S., Lachiri, Z. & Ellouze, N. 2007. Improved one-class SVM classifier for sounds classification. In AVSS 2007. IEEE Conference on Advanced Video and Signal Based Surveillance, 117-122, London
119. Rätsch, G., Mika, S., Schölkopf, B. & Müller, K. R. 2002. Constructing boosting algorithms from SVMS: An approach to one-class classification. IEEE Transaction on Pattern Analysis and Machine Intelligence 24(9), 1184-1199
120. Ritter, G. & Gallegos, M. 1997. Outliers in statistical pattern recognition and an application to automatic chromosome classification. Pattern Recognition Letters 18, 525-539
121. Rocchio, J. 1971. Relevant feedback in information retrieval. In In The Smart Retrieval System-experiments in automatic document processing. Englewood Cliffs
122. Rodriguez, B. M., Peterson, G. L. & Agaian, S. S. 2007. Steganography anomaly detection using simple one-class classification. In Proceddings of the SPIE, 6579, page 65790E.
123. Rubin, D. B. 1987. Multiple Imputation for Non response in Surveys. John Wiley and Sons, New York
124. Rumelhart, D. E. & McClelland, J. L. 1986. Parallel distributed processing : Exploration in the microstructure of cognition, volume 1 & 2. MIT Press
125. Sachs, A., Thiel, C. & Schwenker, F. 2006. One-class support vector machines for the classification of bioacoustic time series. In INFOS'06, Cairo
126. Sarmiento, T., Hong, S. J. & May, G. S. 2005. Fault detection in reactive ion etching systems using one-class, support vector machines. In Advanced Semiconductor Manufacturing Conference and Workshop, 139-142, Munich
127. Schapire, R. E., Feund, Y., Bartlett, P. L. & Lee, W. S. 1998. Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics 26(5), 1651-1686
128. Schneider, K. M. 2004. Learning to filter junk e-mail from positive and unlabeled examples. In Lecture Notes in Computer Science, Su, K-Y., Tsujii, J., Lee, J-H. & Kwong, O. Y. (eds). 3248, 426-435. Springer
129. Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J. & Williamson, R. C. 1999b. Estimating the support of a high dimensional distribution. Technical Report MSR-TR-99-87, Microsoft Research
130. Schölkopf, B., Smola, A. J. & Müller, K. R. 1998. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10, 1299-1319
131. Schölkopf, B., Williamson, R. C., Smola, A. J., Taylor, J. S. & Platt, J.C. 2000. Support vector method for novelty detection. In Neural Information Processing Systems, 582-588, CO, USA
132. Schölkopf, B., Williamson, R. C., Smola, A. J. & Taylor, J. S. 1999a. SV estimation of a distribution's support. In Advances in Neural Information Processing Systems, CO, USA
133. Seguì, S., Igual, L. & Vitrià, J. 2010. Weighted bagging for graph based one-class classifiers. In Multiple Classifier Systems, Neamat Gayar, Josef Kittler, & Fabio Roli, (eds). Springer, 5997, 1-10
134. Seo, K. 2007. An application of one-class support vector machines in content based image retrieval. Expert Systems with Applications 33(2), 491-498
135. Sharkey, A. J. C. & Sharkey, N. E. 1995. How to improve the reliability of artificial neural networks. Technical Report CS-95-11, Department of Computer Science, University of Sheffield
136. Shieh, A. D. & Kamm, D. F. 2009. Ensembles of one class support vector machines. In Lecture Notes in Computer Science, Benediktsson, J., Kittler, J. & Roli, F. (eds). 5519, 181-190. Springer-Verlag
137. Shin, H. J., Eom, D. W. & Kim, S. S. 2005. One-class support vector machines: An application in machine fault detection and classification. Computers and Industrial Engineering 48(2), 395-408
138. Silva, J. & Willett, R. 2009. Hypergraph-based anomaly detection of high-dimensional co-occurrences. IEEE transactions on pattern analysis and machine intelligence 31(3), 563-569
139. Skabar, A. 2003. Single-class classifier learning using neural networks: An application to the prediction of mineral deposits. In Proceedings of the Second International Conference on Machine Learning and Cybernetics, 4, 2127-2132, China
140. Spinosa, E. J. & Ferreira De Carvalho, A. C. P. L. 2004. SVMs for novel class detection in bioinformatics. In Brazilian Workshop on Bioinformatics, 81-88, Brazil
141. Srebro, N. & Jaakkola, T. 2003. Weighted low-rank approximations. In Proceedings of the 20th International Conference on Machine Learning, Fawcett, T. & Mishra, N. (eds). AAAI Press, Washington DC, USA, 720-727
142. Sun, D., Tran, Q. A., Duan, H. & Zhang, G. 2005. A novel method for Chinese spam detection based on one-class support vector machine. Journal of Information and Computational Science 2(1), 109-114
143. Tang, Y. & Yang, Z. 2005. One-class classifier for HFGWR ship detection using similarity-dissimilarity representation. In Proceedings of the 18th International Conference on Innovations in Applied Artificial Intelligence, Ali, M. & Esposito, F. (eds). Springer-Verlag, Italy, 432-441
144. Tanigushi, M. & Tresp, V. 1997. Averaging regularized estimators. Neural Computation 9, 1163-1178
145. Tax, D. M. J. 2001. One-class Classification. PhD thesis, Delft University of Technology
146. Tax, D. M. J. & Duin, R. P. W. 1999a. Data domain description using support vectors. In Proceedings of European Sysmposium on Artificial Neural Networks, Brussels, 251-256
147. Tax, D. M. J. & Duin, R. P. W. 1999b. Support vector domain description. Pattern Recognition Letters 20, 1191-1199
148. Tax, D. M. J. & Duin, R. P. W. 2000. Data description in subspaces. In Proceedings of 15th International Conference on Pattern Recognition, Los Alamitos, 672-675
149. Tax, D. M. J. & Duin, R. P. W. 2001a. Combining one class classifiers. In Proceedings of the 2nd International Workshop on Multiple Classifier Systems, 299-308, Cambridge, U.K.
150. Tax, D. M. J. & Duin, R. P. W. 2001b. Uniform object generation for optimizing one-class classifiers. Journal of Machine Learning Research 2, 155-173
151. Tax, D. M. J. & Duin, R. P. W. 2004. Support vector data description. Machine Learning 54(1), 45-66
152. Tax, D. M. J., Ypma, A. & Duin, R. P. W. 1999. Support vector data description applied to machine vibration analysis. In Proceedings of the 5th Annual Conference of the ASCI, 398-405, The Netherlands
153. Tian, J. & Gu, H. 2010. Anomaly detection combining one-class SVMs and particle swarm optimization algorithms. Nonlinear Dynamics 61(1-2), 303-310
154. Tran, Q. A., Duan, H. & Li, X. 2004. One-class support vector machine for anomaly network traffic detection. In the 2nd Network Research Workshop of the 18th APAN, Cairns, Australia
155. Tu, Y., Li, G. & Dai, H. 2006. Integrating local one-class classifiers for image retrieval. In Advanced Data Mining and Applications, X. Li, O. R. Zaïane, & Z. H. Li (eds), Springer, 4093, 213-222
156. Valiant, L. G. 1984. A theory of learnable. Coomunications of the ACM 270(11), 1134-1142
157. Villalba, S. D. & Cunningham, P. 2007. An evaluation of dimension reduction techniques for one-class classification. Artificial Intelligence Review 270(4), 273-294
158. Wang, C., Ding, C., Meraz, R. F. & Holbrook, S. R. 2006. PSoL: A positive sample only learning algorithm for finding non-coding RNA genes. BioInformatics 22(21), 2590-2596
159. Wang, K. & Stolfo, S. J. 2003. One class training for masquerade detection. In ICDM Workshop on Data Mining for Computer Security
160. Wang, Q. H., Lopes, L. S. & Tax, D. M. J. 2004. Visual object recognition through one-class learning. In Image Analysis and Recognition, Campilho, A. C. & Kamel, M. S. (eds). Lecture Notes in Computer Science 3211, 463-470. Springer
161. Wolpert, D. 1992. Stacked generalization. Neural Networks 5, 241-259
162. Wu, C. T., Cheng, K. T., Zhu, Q. & Wu, Y. L. 2005. Using visual features for anti spam filtering. In Proceedings of IEEE International Conference on Image Processing, 3, 509-12, Italy
163. Xiaomu, S., Guoliang, F. & Rao, M. 2008. SVM-Based data editing for enhanced one-class classification of remotely sensed imagery. IEEE Geoscience and Remote Sensing Letters 5, 189-193
164. Xu, Y. & Brereton, R. G. 2007. Automated single-nucleotide polymorphism analysis using fluorescence excitation-emission spectroscopy and one-class classifiers. Analytical and Bioanalytical Chemistry 388(3), 655-664
165. Yang, L. & Madden, M. G. 2007. One-class support vector machine calibration using particle swarm optimisation. In AICS 2007, Dublin
166. Yang, J., Wang, S., Chen, N., Chen, X. & Shi, P. 2010a. Wearable accelerometer based extendable activity recognition system. In 2010 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 3641-3647, Alaska, USA
167. Yang, J., Zhong, N., Liang, P., Wang, J., Yao, Y. & Lu, S. 2010b. Brain activation detection by neighborhood one-class SVM. Cognitive Systems Research 11(1), 16-24
168. Yasutoshi, Y. 2006. One-class support vector machines for recommendation tasks. In PKDD, Ng, W. K., Kitsuregawa, M., Li, J. & Chang, K. (eds). 3918. Springer, Singapore, 230-239
169. Yilmazel, O., Symonenko, S., Balasubramanian, N. & Liddy, E. D. 2005. Leveraging one-class SVM and semantic analysis to detect anomalous content. In IEEE International Conference on Intelligence and Security Informatics, Kantor, P. B., Muresan, G., Roberts, F. S., Zeng, D. D., Wang, F-Y., Chen, H. & Merkle, R. C. (eds). 3495, Springer, 381-388
170. Yousef, M., Jung, S., Showe, L.C. & Showe, M.K. 2008. Learning from positives examples when the negative class is undermined-microRNA gene identification. Algorithms for Molecular Biology 3(2), 1-9
171. Ypma, A. & Duin, R. P. W. 1998. Support objects for domain approximation. In Proceedings of the 8th International Conference on Artificial Neural Networks, Sweden
172. Yu, H. 2003. SVMC: Single-class classification with support vector machines. In Proceedings of International Joint Conference on Artificial Intelligence, 567-572, Mexico
173. Yu, H. 2005. Single-class classification with mapping convergence. Machine Learning 610(1), 49-69
174. Yu, H., Han, J. & Chang, K. C. C. 2002. PEBL: Positive-example based learning for web page classification using SVM. In Eighth International Conference on Knowledge Discovery and Data Mining, 239-248. Alberta, Canada
175. Yu, H., Han, J. & Chang, K. C. C. 2004. PEBL: Web page classification without negative examples. IEEE Transactions on Knowledge and Data Engineering 16(1), 70-81
176. Yu, H., Zhai, C. X. & Han, J. 2003. Text classification from positive and unlabeled documents. In Proceedings of the 12th International Conference on Information and Knowledge Management, 232-239. Louisiana, USA
177. Zeng, Z., Fu, Y., Roisman, G. I., Wen, Z., Hu, Y. & Huang, T. S. 2006. One-class classification for spontaneous facial expression analysis. In Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition, 281-286, Southampton, U.K.
178. Zhang, B. & Zuo, W. 2008. Learning from positive and unlabeled examples: A survey. In 2008 International Symposiums on Information Processing ISIP, 650-654, Russia
179. Zhang, D., Cai, L., Wang, Y. & Zhang, L. 2010. A learning algorithm for one-class data stream classification based on ensemble classifier. In 2010 International Conference on Computer Application and System Modeling (ICCASM), IEEE, 2. 596-600
180. Zhang, J., Lu, J. & Zhang, G. 2011. Combining one class classification models for avian influenza outbreaks. In 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MDCM), 190-196, Paris. IEEE
181. Zhang, R., Zhang, S., Muthuraman, S. & Jiang, J. 2007. One class support vector machine for anomaly detection in the communication network performance data. In Proceedings of the 5th Conference on Applied Electromagnetics, Wireless and Optical Communications, Spain, 31-37
182. Zhang, Y., Li, X. & Orlowska, M. 2008. One-class classification of text streams with concept drift. In IEEE International Conference on Data Mining Workshops, 2008. ICDMW '08. IEEE, 116-125, Italy
183. Zhao, Y., Li, B., Li, X., Liu, W. & Ren, S. 2005. Customer churn prediction using improved one-class support vector machine. In Advanced Data Mining and Applications, Lecture Notes in Computer Science 3584, 300-306
184. Zhou, J., Chan, K. L., Chong, V. F. H. & Krishnan, S. M. 2005. Extraction of brain tumor from MR images using one-class support vector machine. In Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, 1-4
185. Zhu, X. 2005. Semi-supervised learning literature survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison..