Computer Science ›› 2013, Vol. 40 ›› Issue (12): 70-74.

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Sampling Techniques with CBES for Imbalanced Learning

ZHI Wei-mei,GUO Hua-ping and FAN Ming   

  • Online:2018-11-16 Published:2018-11-16

Abstract: CBES is a method which can be used for classification of imbalanced datasets.Related experimental results show CBES can boost the generalization ability of the base classifier.Reported researches show sampling method can effectively improve the performance of rare data.In the paper,we skillfully used sampling methods into CBES,and then proposed a method,named sampling-based CBES (SCBES) to further improve the classification performance of rare data.The experimental results demonstrate SCBES can effectively improve the performance of classification for imbalanced datasets.

Key words: Imbalanced data sets,Ensemble,Ensemble selection,Sampling method

[1] He Hai-bo,Garcia,Edwardo A.Learning from imbalanced Data[J].IEEE Transactions on Knowledge and Data Engineering,2009,21(9):1263-1284
[2] Fawcett T,Provost F.Combining Data Mining and MachineLearning for Effective User Profile[C]∥Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining.Portland,Oregon,USA,1996:8-13
[3] Ezawa K J,Singh M,Norton S W.Learning Goal OrientedBayesian Networks for Telecommunications Risk Management[C]∥Proceedings of the International Conference on Machine Learning.Bari,Italy,1996:139-147
[4] Zheng Zhaohui,Wu Xiaoyun,Srihari Rohini.Feature Selection for Text Categorization on Imbalanced Data[J].SIGKDD Explorations,2004,6(1):80-89
[5] 黄浩,何钦铭,陈奇,等.基于加权边界度的稀有类检测算法[J].软件学报,2012,23(5):1195-1208
[6] 职为梅,郭华平,张银峰,等.一种面向非平衡数据集分类问题的组合选择方法[J].小型微型计算机系统,2014,35
[7] 高嘉伟,梁吉业.非平衡数据集分类研究问题进展[J].计算机科学,2008,35(4):10-13
[8] Breiman L.Bagging predictors[J].Machine Learning,1996,24(2):123-140
[9] Freund Y,Schapire R F.A decision-theoretic generalization ofon-line learning and an application to boosting[J].Journal of Computer and System Sciences,1997,55(1):119-139
[10] Breiman L.Random forests[J].Machine learning,2001,45(1):5-32
[11] Rodriguez J J,Kuncheva L I,Alonso C J.Rotation forest:A newclassifier ensemble method[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(10):1619-1630
[12] Sun Yan-min,Mobamed S K,Wong A K C.Cost-sensitive boosting for classification of imbalanced data[J].Pattern Recognition,2007,40(12):3358-3378
[13] Chawla N V,Bowyer K W,Hall L O,et al.SMOTE:Synthetic Minority Over-Sampling Technique[J].Journal of Artificial Intelligence Research,2002,16:321-357
[14] Han Hui,Wang Wen-yuan,Mao Bing-huan.Borderline-SMOTE:A new over-sampling method in imbalanced data sets learning[C]∥Proceedings of International Conference on Intelligent Computing.Hefei,China,2005:878-887
[15] Zhi Wei-mei,Guo Hua-ping,Fan Ming.Energy-Based Metric for Ensemble Selection[C]∥Proceedings of 14th Asia-Pacific Web Conference.Kunming,China,2012:306-317
[16] 曾志强,吴群,廖备水,等.一种基于核SMOTE的非平衡数据集分类方法[J].电子学报,2009,37(11):2489-2495
[17] UCI repository of machine learning databases[EB/OL].http://www.ics.uci.edu/~mlearn/ MLRepository.html.

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