Feature and Classifier Selection in Class Decision Trees

Authors:
Kazuaki Aoki;Mineichi Kudo
Affiliations:
Division of Computer Science Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan 060-0814;Division of Computer Science Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan 060-0814
Venue:
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2008

Citing 7
Cited 0

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Abstract

Feature selection is an important technique in pattern recognition. By removing features that have little or no discriminative information, it is possible to improve the predictive performance of classifiers and to reduce the measuring cost of features. In general, feature selection algorithms choose a common feature subset useful for all classes. However, in general, the most contributory feature subsets vary depending on classes relatively to the other classes. In this study, we propose a classifier as a decision tree in which each leaf corresponds to one class and an internal node classifies a sample to one of two class subsets. We also discuss classifier selection in each node.