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| Clustering Ensembles Based Classification Method for Imbalanced Data Sets |
| CHEN Si,GUO Gong-De,CHEN Li-Fei |
School of Mathematics and Computer Science,Fujian Normal University,Fuzhou 350007
Key Laboratory of Network Security and Cryptography,Fujian Normal University,Fuzhou 350007 |
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Abstract Recently, classification of imbalanced data sets becomes a research hotspot in data mining and machine learning. A type of novel classification methods for imbalanced data sets based on clustering ensembles is proposed, which aims to provide a better training platform for classification methods by introducing clustering consistency index to find the cluster boundary minority examples and the cluster center majority examples. And the improved synthetic minority over-sampling technique (SMOTE) and the modified random under-sampling method are used respectively to deal with imbalanced data sets. The classifications of eight methods on some public data sets are compared. Experimental results show that the proposed methods perform better for both minority and majority classes, and are effective and feasible to deal with the imbalanced data sets.
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Received: 20 October 2009
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2010, Vol. 23 
