Greedy active learning algorithm for logistic regression models

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

研究成果: Article同行評審

3 引文 斯高帕斯(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.

頁(從 - 到)119-134
期刊Computational Statistics and Data Analysis
出版狀態Published - 2019 1月

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 計算數學
  • 計算機理論與數學
  • 應用數學


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