An ensemble of decision cluster crotches for classification of high dimensional data

Authors:
Yan Li;Yunming Ye;Zhaocai Sun;Edward Hung;Joshua Huang;Yueping Li
Affiliations:
Shenzhen Polytechnic, Liuxian Road, Shenzhen 518055, China;Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China and Shenzhen Key Laboratory of Internet Information Collaboration, Shenzhen 518055, China;Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China and Shenzhen Key Laboratory of Internet Information Collaboration, Shenzhen 518055, China;Department of Computing, The Hong Kong Polytechnic University, Kow Loon, Hong Kong;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Xili, Shenzhen 518055, China;Shenzhen Polytechnic, Liuxian Road, Shenzhen 518055, China
Venue:
Knowledge-Based Systems
Year:
2013

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Abstract

This paper presents a Crotch Ensemble classification model for high dimensional data. A Crotch Ensemble is obtained from a decision cluster tree built by calling a clustering algorithm recursively. A crotch is an inner node of the tree together with its direct children. If the children of a crotch have more than one dominant class, the crotch is defined as a crotch predictor. Each crotch predictor constructs a classifier by itself. A Crotch Ensemble consists of a set of crotch predictors. When classifying a new object, a subset of crotch predictors is selected according to the distances between the object and the crotch predictors. A classification is made on the object as the class predicted by the crotch predictors with the maximum accumulative weights. The experimental results on both synthetic and real data have shown that the Crotch Ensemble model can get better classification results on high dimensional data than other classification methods.