Feature and Classifier Selection in Class Decision Trees

  • Kazuaki Aoki
  • Mineichi Kudo
Conference paper

DOI: 10.1007/978-3-540-89689-0_60

Volume 5342 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Aoki K., Kudo M. (2008) Feature and Classifier Selection in Class Decision Trees. In: da Vitoria Lobo N. et al. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2008. Lecture Notes in Computer Science, vol 5342. Springer, Berlin, Heidelberg

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Kazuaki Aoki
    • 1
  • Mineichi Kudo
    • 1
  1. 1.Division of Computer Science Graduate School of Information Science and TechnologyHokkaido UniversitySapporoJapan