A hierarchical shrinking decision tree for imbalanced datasets

Authors:
Chien-I Lee;Cheng-Jung Tsai;Chiu-Ting Chen
Affiliations:
Department of Information and Learning Technology, National University of Tainan, Tainan. Taiwan, ROC;Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan, ROC;Department of Information and Learning Technology, National University of Tainan, Tainan. Taiwan, ROC
Venue:
DNCOCO'06 Proceedings of the 5th WSEAS international conference on Data networks, communications and computers
Year:
2006

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Abstract

Since the real-world datasets are often predominately composed of majority examples with only a small percentage of minority/interesting examples, data mining researchers have put more and more attention on developing efficient approaches to handle the imbalanced datasets. In this paper, we proposed Hierarchical Shrinking decision tree algorithm, called Hshrink, to solve the class imbalance problem. HShrink hierarchically groups minority examples together by using the splitting function derived from geometric mean in each internal node of the decision tree. Consequently, HShrink can accurately mine the rules of minority examples and reach a higher predicted accurately.