Greedy active learning algorithm for logistic regression models

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

Computational Statistics & Data Analysis, 2019, vol. 129, issue C, 119-134

Abstract: 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.

Keywords: Active learning algorithm; D-efficiency criterion; Forward selection; Graft optimization (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:129:y:2019:i:c:p:119-134

DOI: 10.1016/j.csda.2018.08.013

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