Greedy active learning algorithm for logistic regression models

Hsiang Ling Hsu, Yuan chin Ivan Chang, Ray Bing Chen

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


We study a logistic model-based active learning procedure for binary classification problems, in which we adopt a batch subject selection strategy with a modified sequential experimental design method. Moreover, accompanying the proposed subject selection scheme, we simultaneously conduct a greedy variable selection procedure such that we can update the classification model with all labeled training subjects. The proposed algorithm repeatedly performs both subject and variable selection steps until a prefixed stopping criterion is reached. Our numerical results show that the proposed procedure has competitive performance, with smaller training size and a more compact model compared with that of the classifier trained with all variables and a full data set. We also apply the proposed procedure to a well-known wave data set (Breiman et al., 1984) and a MAGIC gamma telescope data set to confirm the performance of our method.

Original languageEnglish
Pages (from-to)119-134
Number of pages16
JournalComputational Statistics and Data Analysis
Publication statusPublished - 2019 Jan

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics


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