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Greedy active learning algorithm for logistic regression models

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

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.

Suggested Citation

  • Hsu, Hsiang-Ling & Chang, Yuan-chin Ivan & Chen, Ray-Bing, 2019. "Greedy active learning algorithm for logistic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 119-134.
    • Handle: RePEc:eee:csdana:v:129:y:2019:i:c:p:119-134
    DOI: 10.1016/j.csda.2018.08.013
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    References listed on IDEAS

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    1. Deng, Xinwei & Joseph, V. Roshan & Sudjianto, Agus & Wu, C. F. Jeff, 2009. "Active Learning Through Sequential Design, With Applications to Detection of Money Laundering," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 969-981.
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
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