Exploiting the categorical reliability difference for binary classification

Lei Sun, Kar Ann Toh, Badong Chen, Zhiping Lin

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


In binary pattern classification, the reliabilities of statistics obtained from the samples of the two categories are generally different. When the statistics are used for modeling a classifier, such reliability difference could impact the generalization performance. We formulate a disparity index to show the statistical disparity based on the generalized eigenvalue decomposition of the categorical moment matrices. It is shown that this disparity index can effectively indicate the reliability difference between the two categories. The obtained reliability difference is subsequently utilized to adjust the regularization term of a classifier for effective learning generalization. Our experiments based on 10 real-world benchmark data sets validate the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)2022-2040
Number of pages19
JournalJournal of the Franklin Institute
Issue number4
Publication statusPublished - 2018 Mar

Bibliographical note

Publisher Copyright:
© 2017 The Franklin Institute

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Applied Mathematics


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