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Exploiting Heterogeneous Features for Classification Learning

  • Yiqiu Han
  • Wai Lam
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2690)

Abstract

This paper proposes a framework for handling heterogeneous features containing hierarchical values and texts under Bayesian learning. To exploit hierarchical features, we make use of a statistical technique called shrinkage. We also explore an approach for utilizing text data to improve classification performance. We have evaluated our framework using a yeast gene data set which contain hierarchical features as well as text data.

Keywords

hierarchical features Bayesian learning parameter estimation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Yiqiu Han
    • 1
  • Wai Lam
    • 1
  1. 1.Department of Systems Engineering and Engineering ManagementThe Chinese University of Hong KongShatin, Hong Kong

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