Clustering Based Bagging Algorithm on Imbalanced Data Sets

The approach of under-sampling the majority class is an effective method in dealing with classifying imbalanced data sets, but it has the deficiency of ignoring useful information. In order to eliminate this deficiency, we propose a Clustering Based Bagging Algorithm (CBBA). In CBBA, the majority class is clustered into several groups and instances are randomly sampled from each group. Those sampled instances are combined together with the minority class instances, and are used to train a base classifier. Final predictions are produced by combining those classifiers. The experimental results show that our approach outperforms the under-sampling method.

[1]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[2]  Hisashi Kashima,et al.  Roughly balanced bagging for imbalanced data , 2009, Stat. Anal. Data Min..

[3]  Taeho Jo,et al.  Class imbalances versus small disjuncts , 2004, SKDD.

[4]  Yi Lin,et al.  Support Vector Machines for Classification in Nonstandard Situations , 2002, Machine Learning.

[5]  Yue-Shi Lee,et al.  Cluster-Based Sampling Approaches to Imbalanced Data Distributions , 2006, DaWaK.

[6]  Hui Han,et al.  Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning , 2005, ICIC.

[7]  John Langford,et al.  Cost-sensitive learning by cost-proportionate example weighting , 2003, Third IEEE International Conference on Data Mining.

[8]  Cen Li,et al.  Classifying imbalanced data using a bagging ensemble variation (BEV) , 2007, ACM-SE 45.

[9]  Xiao-Ping Zhang,et al.  Advances in Intelligent Computing, International Conference on Intelligent Computing, ICIC 2005, Hefei, China, August 23-26, 2005, Proceedings, Part I , 2005, ICIC.

[10]  Salvatore J. Stolfo,et al.  AdaCost: Misclassification Cost-Sensitive Boosting , 1999, ICML.

[11]  Nitesh V. Chawla,et al.  SMOTEBoost: Improving Prediction of the Minority Class in Boosting , 2003, PKDD.

[12]  Herna L. Viktor,et al.  Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach , 2004, SKDD.

[13]  Gary M. Weiss.  Mining with rarity: a unifying framework , 2004, SKDD.

[14]  Zhi-Hua Zhou,et al.  Exploratory Under-Sampling for Class-Imbalance Learning , 2006, ICDM.

[15]  Hendrik Blockeel,et al.  Knowledge Discovery in Databases: PKDD 2003 , 2003, Lecture Notes in Computer Science.

[16]  Yang Wang,et al.  Cost-sensitive boosting for classification of imbalanced data , 2007, Pattern Recognit..

[17]  H. Kashima,et al.  Roughly balanced bagging for imbalanced data , 2009 .

[18]  Stan Matwin,et al.  Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.

[19]  Zhi-Hua Zhou,et al.  Exploratory Under-Sampling for Class-Imbalance Learning , 2006, Sixth International Conference on Data Mining (ICDM'06).

[20]  Edward Y. Chang,et al.  KBA: kernel boundary alignment considering imbalanced data distribution , 2005, IEEE Transactions on Knowledge and Data Engineering.

[21]  Robert C. Holte,et al.  C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling , 2003 .

[22]  Xuelong Li,et al.  Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.