메뉴 건너뛰기




Volumn 32, Issue 1, 2018, Pages 25-82

Imbalanced classification in sparse and large behaviour datasets

Author keywords

Behaviour data; Cost sensitive learning; Imbalanced learning; On line repository; Over and undersampling; Support vector machine (SVM)

Indexed keywords

CLASSIFICATION (OF INFORMATION); SUPPORT VECTOR MACHINES;

EID: 85021059612     PISSN: 13845810     EISSN: 1573756X     Source Type: Journal    
DOI: 10.1007/s10618-017-0517-y     Document Type: Article
Times cited : (39)

References (94)
  • 1
    • 22944452794 scopus 로고    scopus 로고
    • Applying support vector machines to imbalanced datasets
    • Pisa, Italy, September 20–24, 2004. Proceedings, Springer, Berlin, pp 39–50
    • Akbani R, Kwek S, Japkowicz N (2004) Applying support vector machines to imbalanced datasets. In: Machine learning: ECML 2004: 15th European conference on machine learning, Pisa, Italy, September 20–24, 2004. Proceedings, Springer, Berlin, pp 39–50. doi:10.1007/978-3-540-30115-8_7
    • (2004) In: Machine learning: ECML 2004: 15th European conference on machine learning
    • Akbani, R.1    Kwek, S.2    Japkowicz, N.3
  • 5
    • 0038209756 scopus 로고    scopus 로고
    • Benchmarking state-of-the-art classification algorithms for credit scoring
    • Baesens B, Van Gestel T, Viaene S, Stepanova M, Suykens J, Vanthienen J (2003) Benchmarking state-of-the-art classification algorithms for credit scoring. J Oper Res Soc 54(6):627–635. doi:10.1057/palgrave.jors.2601545
    • (2003) J Oper Res Soc , vol.54 , Issue.6 , pp. 627-635
    • Baesens, B.1    Van Gestel, T.2    Viaene, S.3    Stepanova, M.4    Suykens, J.5    Vanthienen, J.6
  • 6
    • 0036522693 scopus 로고    scopus 로고
    • Strategies for learning in class imbalance problems
    • Barandela R, Snchez J, Garca V, Rangel E (2003) Strategies for learning in class imbalance problems. Pattern Recognit 36(3):849–851. doi:10.1016/S0031-3203(02)00257-1
    • (2003) Pattern Recognit , vol.36 , Issue.3 , pp. 849-851
    • Barandela, R.1    Snchez, J.2    Garca, V.3    Rangel, E.4
  • 7
    • 37049032887 scopus 로고    scopus 로고
    • Modularity and community detection in bipartite networks
    • Barber MJ (2007) Modularity and community detection in bipartite networks. Phys Rev E 76(066):102. doi:10.1103/PhysRevE.76.066102
    • (2007) Phys Rev E , vol.76 , Issue.66 , pp. 102
    • Barber, M.J.1
  • 8
    • 84891807032 scopus 로고    scopus 로고
    • MWMOTE-majority weighted minority oversampling technique for imbalanced data set learning
    • Barua S, Islam MM, Yao X, Murase K (2014) MWMOTE-majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans Knowl Data Eng 26(2):405–425. doi:10.1109/TKDE.2012.232
    • (2014) IEEE Trans Knowl Data Eng , vol.26 , Issue.2 , pp. 405-425
    • Barua, S.1    Islam, M.M.2    Yao, X.3    Murase, K.4
  • 9
    • 27144531570 scopus 로고    scopus 로고
    • A study of the behavior of several methods for balancing machine learning training data
    • Batista GEAPA, Prati RC, Monard MC (2004) A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor Newsl 6(1):20–29. doi:10.1145/1007730.1007735
    • (2004) SIGKDD Explor Newsl , vol.6 , Issue.1 , pp. 20-29
    • Batista, G.E.A.P.A.1    Prati, R.C.2    Monard, M.C.3
  • 10
    • 84958068648 scopus 로고    scopus 로고
    • Improved community detection in weighted bipartite networks
    • Beckett SJ (2016) Improved community detection in weighted bipartite networks. R Soc Open Sci 3(1). doi:10.1098/rsos.140536
    • (2016) R Soc Open Sci , vol.3 , Issue.1
    • Beckett, S.J.1
  • 11
    • 84929326583 scopus 로고    scopus 로고
    • Evaluation measures for models assessment over imbalanced data sets
    • Bekkar M, Djemaa HK, Alitouche TA (2013) Evaluation measures for models assessment over imbalanced data sets. J Inf Eng Appl 3(10):27–38
    • (2013) J Inf Eng Appl , vol.3 , Issue.10 , pp. 27-38
    • Bekkar, M.1    Djemaa, H.K.2    Alitouche, T.A.3
  • 12
    • 78651086789 scopus 로고    scopus 로고
    • Data mining for credit card fraud: a comparative study
    • Bhattacharyya S, Jha S, Tharakunnel K, Westland JC (2011) Data mining for credit card fraud: a comparative study. Decis Support Syst 50(3):602–613. doi:10.1016/j.dss.2010.08.008
    • (2011) Decis Support Syst , vol.50 , Issue.3 , pp. 602-613
    • Bhattacharyya, S.1    Jha, S.2    Tharakunnel, K.3    Westland, J.C.4
  • 15
    • 70449521414 scopus 로고    scopus 로고
    • Recommender system for online dating service
    • In:, Conference, VSB, Ostrava
    • Brozovsky L, Petricek V (2007) Recommender system for online dating service. In: Proceedings of Znalosti 2007 Conference, VSB, Ostrava
    • (2007) Proceedings of Znalosti , pp. 2007
    • Brozovsky, L.1    Petricek, V.2
  • 17
    • 37949004300 scopus 로고    scopus 로고
    • Data mining for imbalanced datasets: an overview
    • Maimon O, Rokach L, (eds), Springer, Boston
    • Chawla NV (2005) Data mining for imbalanced datasets: an overview. In: Maimon O, Rokach L (eds) Data mining and knowledge discovery handbook. Springer, Boston, pp 853–867
    • (2005) Data mining and knowledge discovery handbook , pp. 853-867
    • Chawla, N.V.1
  • 19
    • 9444297357 scopus 로고    scopus 로고
    • Smoteboost: improving prediction of the minority class in boosting
    • Lavrač N, Gamberger D, Todorovski L, Blockeel H, (eds), Springer, Berlin
    • Chawla NV, Lazarevic A, Hall LO, Bowyer KW (2003) Smoteboost: improving prediction of the minority class in boosting. In: Lavrač N, Gamberger D, Todorovski L, Blockeel H (eds) Knowledge discovery in databases: PKDD. Springer, Berlin, pp 107–119
    • (2003) Knowledge discovery in databases: PKDD , pp. 107-119
    • Chawla, N.V.1    Lazarevic, A.2    Hall, L.O.3    Bowyer, K.W.4
  • 20
    • 27144549260 scopus 로고    scopus 로고
    • Editorial: special issue on learning from imbalanced data sets
    • Chawla NV, Japkowicz N, Kotcz A (2004) Editorial: special issue on learning from imbalanced data sets. SIGKDD Explor Newsl 6(1):1–6. doi:10.1145/1007730.1007733
    • (2004) SIGKDD Explor Newsl , vol.6 , Issue.1 , pp. 1-6
    • Chawla, N.V.1    Japkowicz, N.2    Kotcz, A.3
  • 21
    • 84898796363 scopus 로고    scopus 로고
    • Big data: a survey
    • Chen M, Mao S, Liu Y (2014) Big data: a survey. Mob Netw Appl 19(2):171–209. doi:10.1007/s11036-013-0489-0
    • (2014) Mob Netw Appl , vol.19 , Issue.2 , pp. 171-209
    • Chen, M.1    Mao, S.2    Liu, Y.3
  • 23
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7(Jan):1–30
    • (2006) J Mach Learn Res , vol.7 , Issue.Jan , pp. 1-30
    • Demšar, J.1
  • 28
    • 33646023117 scopus 로고    scopus 로고
    • An introduction to ROC analysis
    • Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27(8):861–874. doi:10.1016/j.patrec.2005.10.010
    • (2006) Pattern Recognit Lett , vol.27 , Issue.8 , pp. 861-874
    • Fawcett, T.1
  • 29
    • 20444364008 scopus 로고    scopus 로고
    • Comparison of distance measures in cluster analysis with dichotomous data
    • Finch H (2005) Comparison of distance measures in cluster analysis with dichotomous data. J Data Sci 3(1):85–100
    • (2005) J Data Sci , vol.3 , Issue.1 , pp. 85-100
    • Finch, H.1
  • 30
    • 74049087026 scopus 로고    scopus 로고
    • Community detection in graphs
    • Fortunato S (2010) Community detection in graphs. Phys Rep 486(35):75–174. doi:10.1016/j.physrep.2009.11.002
    • (2010) Phys Rep , vol.486 , Issue.35 , pp. 75-174
    • Fortunato, S.1
  • 31
    • 84875251066 scopus 로고    scopus 로고
    • A neural network algorithm for semi-supervised node label learning from unbalanced data
    • Frasca M, Bertoni A, Re M, Valentini G (2013) A neural network algorithm for semi-supervised node label learning from unbalanced data. Neural Netw 43:84–98. doi:10.1016/j.neunet.2013.01.021
    • (2013) Neural Netw , vol.43 , pp. 84-98
    • Frasca, M.1    Bertoni, A.2    Re, M.3    Valentini, G.4
  • 32
    • 84944811700 scopus 로고
    • The use of ranks to avoid the assumption of normality implicit in the analysis of variance
    • Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675–701
    • (1937) J Am Stat Assoc , vol.32 , Issue.200 , pp. 675-701
    • Friedman, M.1
  • 34
    • 84966280501 scopus 로고    scopus 로고
    • A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data
    • Goldstein M, Uchida S (2016) A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PLoS ONE 11(4):1–31. doi:10.1371/journal.pone.0152173
    • (2016) PLoS ONE , vol.11 , Issue.4 , pp. 1-31
    • Goldstein, M.1    Uchida, S.2
  • 35
    • 84872050981 scopus 로고    scopus 로고
    • Characterization and detection of taxpayers with false invoices using data mining techniques
    • Gonzlez PC, Velsquez JD (2013) Characterization and detection of taxpayers with false invoices using data mining techniques. Exp Syst Appl 40(5):1427–1436. doi:10.1016/j.eswa.2012.08.051
    • (2013) Exp Syst Appl , vol.40 , Issue.5 , pp. 1427-1436
    • Gonzlez, P.C.1    Velsquez, J.D.2
  • 36
    • 34548821644 scopus 로고    scopus 로고
    • Module identification in bipartite and directed networks
    • Guimerà R, Sales-Pardo M, Amaral LAN (2007) Module identification in bipartite and directed networks. Phys Rev E 76(036):102. doi:10.1103/PhysRevE.76.036102
    • (2007) Phys Rev E , vol.76 , Issue.36 , pp. 102
    • Guimerà, R.1    Sales-Pardo, M.2    Amaral, L.A.N.3
  • 37
    • 27144479454 scopus 로고    scopus 로고
    • Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach
    • Guo H, Viktor HL (2004) Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach. SIGKDD Explor Newsl 6(1):30–39. doi:10.1145/1007730.1007736
    • (2004) SIGKDD Explor Newsl , vol.6 , Issue.1 , pp. 30-39
    • Guo, H.1    Viktor, H.L.2
  • 38
    • 57649123451 scopus 로고    scopus 로고
    • On the class imbalance problem. In: 2008 fourth international conference on natural computation
    • Y, G, vol
    • Guo X, Yin Y, Dong C, Yang G, Zhou G (2008) On the class imbalance problem. In: 2008 fourth international conference on natural computation, IEEE, vol 4, pp 192–201. doi:10.1109/ICNC.2008.871
    • (2008) IEEE , vol.4 , pp. 192-201
    • Yin, G.X.1    Yang, D.C.2    Zhou, G.3
  • 39
    • 27144501672 scopus 로고    scopus 로고
    • Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning
    • Huang D, Zhang X-P, Huang G-B, (eds), Springer, Berlin
    • Han H, Wang WY, Mao BH (2005) Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Huang D, Zhang X-P, Huang G-B (eds) Advances in intelligent computing. Springer, Berlin, pp 878–887
    • (2005) Advances in intelligent computing , pp. 878-887
    • Han, H.1    Wang, W.Y.2    Mao, B.H.3
  • 40
    • 68549133155 scopus 로고    scopus 로고
    • Learning from imbalanced data
    • He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263–1284. doi:10.1109/TKDE.2008.239
    • (2009) IEEE Trans Knowl Data Eng , vol.21 , Issue.9 , pp. 1263-1284
    • He, H.1    Garcia, E.A.2
  • 42
    • 0002294347 scopus 로고
    • A simple sequentially rejective multiple test procedure
    • Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6(2):65–70
    • (1979) Scand J Stat , vol.6 , Issue.2 , pp. 65-70
    • Holm, S.1
  • 43
    • 0036505670 scopus 로고    scopus 로고
    • A comparison of methods for multiclass support vector machines
    • Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425. doi:10.1109/72.991427
    • (2002) IEEE Trans Neural Netw , vol.13 , Issue.2 , pp. 415-425
    • Hsu, C.W.1    Lin, C.J.2
  • 45
    • 0001750957 scopus 로고
    • Approximations of the critical region of the Friedman statistic
    • Iman RL, Davenport JM (1980) Approximations of the critical region of the Friedman statistic. Commun Stat Theory Methods 9(6):571–595
    • (1980) Commun Stat Theory Methods , vol.9 , Issue.6 , pp. 571-595
    • Iman, R.L.1    Davenport, J.M.2
  • 46
    • 27144540575 scopus 로고    scopus 로고
    • Class imbalances versus small disjuncts
    • Jo T, Japkowicz N (2004) Class imbalances versus small disjuncts. ACM SIGKDD Explor Newsl 6(1):40–49. doi:10.1145/1007730.1007737
    • (2004) ACM SIGKDD Explor Newsl , vol.6 , Issue.1 , pp. 40-49
    • Jo, T.1    Japkowicz, N.2
  • 47
    • 84991624961 scopus 로고    scopus 로고
    • Predictive modeling with big data: is bigger really better?
    • Junqué de Fortuny E, Martens D, Provost F (2014a) Predictive modeling with big data: is bigger really better? Big Data 1(4):215–226. doi:10.1089/big.2013.0037
    • (2014) Big Data , vol.1 , Issue.4 , pp. 215-226
    • Junqué de Fortuny, E.1    Martens, D.2    Provost, F.3
  • 50
    • 0001972236 scopus 로고    scopus 로고
    • Addressing the curse of imbalanced training sets: one-sided selection
    • In: Morgan Kaufmann Publishers Inc., San Francisco, CA, USA
    • Kubat M, Matwin S (1997) Addressing the curse of imbalanced training sets: one-sided selection. In: Proceedings of the fourteenth international conference on machine learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 179–186
    • (1997) Proceedings of the fourteenth international conference on machine learning , pp. 179-186
    • Kubat, M.1    Matwin, S.2
  • 51
    • 71849108522 scopus 로고    scopus 로고
    • Community detection algorithms: a comparative analysis
    • Lancichinetti A, Fortunato S (2009) Community detection algorithms: a comparative analysis. Phys Rev E 80(056):117. doi:10.1103/PhysRevE.80.056117
    • (2009) Phys Rev E , vol.80 , Issue.56 , pp. 117
    • Lancichinetti, A.1    Fortunato, S.2
  • 53
    • 77955067099 scopus 로고    scopus 로고
    • Weighted area under the receiver operating characteristic curve and its application to gene selection
    • Li J, Fine JP (2010) Weighted area under the receiver operating characteristic curve and its application to gene selection. J R Stat Soc Series C (Appl Stat) 59(4):673–692. doi:10.1111/j.1467-9876.2010.00713.x
    • (2010) J R Stat Soc Series C (Appl Stat) , vol.59 , Issue.4 , pp. 673-692
    • Li, J.1    Fine, J.P.2
  • 54
    • 44649197212 scopus 로고    scopus 로고
    • AdaBoost with SVM-based component classifiers
    • Li X, Wang L, Sung E (2008) AdaBoost with SVM-based component classifiers. Eng Appl Artif Intell 21(5):785–795. doi:10.1016/j.engappai.2007.07.001
    • (2008) Eng Appl Artif Intell , vol.21 , Issue.5 , pp. 785-795
    • Li, X.1    Wang, L.2    Sung, E.3
  • 57
    • 64049108468 scopus 로고    scopus 로고
    • Exploratory undersampling for class-imbalance learning
    • Liu XY, Wu J, Zhou ZH (2009) Exploratory undersampling for class-imbalance learning. IEEE Trans Syst Man Cybern B (Cybern) 39(2):539–550. doi:10.1109/TSMCB.2008.2007853
    • (2009) IEEE Trans Syst Man Cybern B (Cybern) , vol.39 , Issue.2 , pp. 539-550
    • Liu, X.Y.1    Wu, J.2    Zhou, Z.H.3
  • 58
    • 77952238401 scopus 로고    scopus 로고
    • A tutorial on support vector machine-based methods for classification problems in chemometrics
    • Luts J, Ojeda F, Van de Plas R, De Moor B, Van Huffel S, Suykens JA (2010) A tutorial on support vector machine-based methods for classification problems in chemometrics. Anal Chim Acta 665(2):129–145. doi:10.1016/j.aca.2010.03.030
    • (2010) Anal Chim Acta , vol.665 , Issue.2 , pp. 129-145
    • Luts, J.1    Ojeda, F.2    Van de Plas, R.3    De Moor, B.4    Van Huffel, S.5    Suykens, J.A.6
  • 59
    • 34249102504 scopus 로고    scopus 로고
    • Classification in networked data: a toolkit and a univariate case study
    • Macskassy SA, Provost F (2007) Classification in networked data: a toolkit and a univariate case study. J Mach Learn Res 8(May):935–983
    • (2007) J Mach Learn Res , vol.8 , Issue.May , pp. 935-983
    • Macskassy, S.A.1    Provost, F.2
  • 60
    • 84919337364 scopus 로고    scopus 로고
    • Explaining data-driven document classifications
    • Martens D, Provost F (2014) Explaining data-driven document classifications. MIS Q 38(1):73–100
    • (2014) MIS Q , vol.38 , Issue.1 , pp. 73-100
    • Martens, D.1    Provost, F.2
  • 61
    • 85013446601 scopus 로고    scopus 로고
    • Mining massive fine-grained behavior data to improve predictive analytics
    • Martens D, Provost F, Clark J, Junqué de Fortuny E (2016) Mining massive fine-grained behavior data to improve predictive analytics. MIS Q 40(4):869–888
    • (2016) MIS Q , vol.40 , Issue.4 , pp. 869-888
    • Martens, D.1    Provost, F.2    Clark, J.3    Junqué de Fortuny, E.4
  • 62
    • 40649126091 scopus 로고    scopus 로고
    • Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance
    • Mazurowski MA, Habas PA, Zurada JM, Lo JY, Baker JA, Tourassi GD (2008) Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. Neural Netw 21(23):427–436. doi:10.1016/j.neunet.2007.12.031
    • (2008) Neural Netw , vol.21 , Issue.23 , pp. 427-436
    • Mazurowski, M.A.1    Habas, P.A.2    Zurada, J.M.3    Lo, J.Y.4    Baker, J.A.5    Tourassi, G.D.6
  • 63
    • 33947284406 scopus 로고    scopus 로고
    • Boosted classification trees and class probability/quantile estimation
    • Mease D, Wyner AJ, Buja A (2007) Boosted classification trees and class probability/quantile estimation. J Mach Learn Res 8:409–439
    • (2007) J Mach Learn Res , vol.8 , pp. 409-439
    • Mease, D.1    Wyner, A.J.2    Buja, A.3
  • 65
    • 37649028224 scopus 로고    scopus 로고
    • Finding and evaluating community structure in networks
    • Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(026):113. doi:10.1103/PhysRevE.69.026113
    • (2004) Phys Rev E , vol.69 , Issue.26 , pp. 113
    • Newman, M.E.J.1    Girvan, M.2
  • 68
    • 78651084785 scopus 로고    scopus 로고
    • The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature
    • Ngai E, Hu Y, Wong Y, Chen Y, Sun X (2011) The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature. Decis Support Syst 50(3):559–569. doi:10.1016/j.dss.2010.08.006
    • (2011) Decis Support Syst , vol.50 , Issue.3 , pp. 559-569
    • Ngai, E.1    Hu, Y.2    Wong, Y.3    Chen, Y.4    Sun, X.5
  • 69
    • 0003243224 scopus 로고    scopus 로고
    • Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Smola AJ, Bartlett P, Schoelkopf B, Schuurmans D (eds) Advances in large-margin classifiers
    • Platt JC (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Smola AJ, Bartlett P, Schoelkopf B, Schuurmans D (eds) Advances in large-margin classifiers. MIT Press, pp 61–74
    • (1999) MIT Press , pp. 61-74
    • Platt, J.C.1
  • 73
    • 32344438970 scopus 로고    scopus 로고
    • Extreme re-balancing for SVMs: a case study
    • Raskutti B, Kowalczyk A (2004) Extreme re-balancing for SVMs: a case study. SIGKDD Explor Newsl 6(1):60–69. doi:10.1145/1007730.1007739
    • (2004) SIGKDD Explor Newsl , vol.6 , Issue.1 , pp. 60-69
    • Raskutti, B.1    Kowalczyk, A.2
  • 74
    • 39549086558 scopus 로고    scopus 로고
    • Maps of random walks on complex networks reveal community structure
    • Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci 105(4):1118–1123. doi:10.1073/pnas.0706851105
    • (2008) Proc Natl Acad Sci , vol.105 , Issue.4 , pp. 1118-1123
    • Rosvall, M.1    Bergstrom, C.T.2
  • 76
    • 0033281701 scopus 로고    scopus 로고
    • Improved boosting algorithms using confidence-rated predictions
    • Schapire RE, Singer Y (1999) Improved boosting algorithms using confidence-rated predictions. Mach Learn 37(3):297–336. doi:10.1023/A:1007614523901
    • (1999) Mach Learn , vol.37 , Issue.3 , pp. 297-336
    • Schapire, R.E.1    Singer, Y.2
  • 77
    • 85006137683 scopus 로고    scopus 로고
    • Analyzing behavioral big data: methodological, practical, ethical, and moral issues
    • Shmueli G (2017) Analyzing behavioral big data: methodological, practical, ethical, and moral issues. Qual Eng 29(1):57–74. doi:10.1080/08982112.2016.1210979
    • (2017) Qual Eng , vol.29 , Issue.1 , pp. 57-74
    • Shmueli, G.1
  • 81
    • 34547673383 scopus 로고    scopus 로고
    • Cost-sensitive boosting for classification of imbalanced data
    • Sun Y, Kamel MS, Wong AK, Wang Y (2007) Cost-sensitive boosting for classification of imbalanced data. Pattern Recognit 40(12):3358–3378. doi:10.1016/j.patcog.2007.04.009
    • (2007) Pattern Recognit , vol.40 , Issue.12 , pp. 3358-3378
    • Sun, Y.1    Kamel, M.S.2    Wong, A.K.3    Wang, Y.4
  • 83
    • 84937796378 scopus 로고    scopus 로고
    • Enn: Extended nearest neighbor method for pattern recognition [research frontier]
    • Tang B, He H (2015) Enn: Extended nearest neighbor method for pattern recognition [research frontier]. IEEE Comput Intell Mag 10(3):52–60. doi:10.1109/MCI.2015.2437512
    • (2015) IEEE Comput Intell Mag , vol.10 , Issue.3 , pp. 52-60
    • Tang, B.1    He, H.2
  • 86
    • 83955164226 scopus 로고    scopus 로고
    • New insights into churn prediction in the telecommunication sector: a profit driven data mining approach
    • Verbeke W, Dejaeger K, Martens D, Hur J, Baesens B (2012) New insights into churn prediction in the telecommunication sector: a profit driven data mining approach. Eur J Oper Res 218(1):211–229. doi:10.1016/j.ejor.2011.09.031
    • (2012) Eur J Oper Res , vol.218 , Issue.1 , pp. 211-229
    • Verbeke, W.1    Dejaeger, K.2    Martens, D.3    Hur, J.4    Baesens, B.5
  • 88
    • 57849084755 scopus 로고    scopus 로고
    • Transaction aggregation as a strategy for credit card fraud detection
    • Whitrow C, Hand DJ, Juszczak P, Weston D, Adams NM (2009) Transaction aggregation as a strategy for credit card fraud detection. Data Min Knowl Discov 18(1):30–55. doi:10.1007/s10618-008-0116-z
    • (2009) Data Min Knowl Discov , vol.18 , Issue.1 , pp. 30-55
    • Whitrow, C.1    Hand, D.J.2    Juszczak, P.3    Weston, D.4    Adams, N.M.5
  • 90
    • 58349090428 scopus 로고    scopus 로고
    • Cluster-based under-sampling approaches for imbalanced data distributions
    • Yen SJ, Lee YS (2009) Cluster-based under-sampling approaches for imbalanced data distributions. Exp Syst Appl 36(3, Part 1):5718–5727. doi:10.1016/j.eswa.2008.06.108
    • (2009) Exp Syst Appl , vol.36 , Issue.3 , pp. 5718-5727
    • Yen, S.J.1    Lee, Y.S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.