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Activity Number: 250 - Topics in Statistical Learning
Type: Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #328335 Presentation
Title: Greedy Active Learning Algorithm for Logistic Regression Models
Author(s): Ray-Bing Chen* and Hsiang-Ling Hsu and Yuan-Chin Ivan Chang
Companies: National Cheng Kung University, Taiwan and National University of Kaohsiung and Academia Sinica
Keywords: Active learning algorithm; D-eciency criterion; forward selection; graft optimization

We study a logistic model-based active learning procedure for binary classification problems, in which we adopt a batch subject selection strategy with a modi ed 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 classi cation model with all labeled training subjects. The proposed algorithm repeatedly performs both subject and variable selection steps until a pre xed stopping criterion is reached. Our numerical results show that the proposed procedure has competitive performance, with smaller training size and a more compact model, comparing 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) to con rm the performance of our method.

Authors who are presenting talks have a * after their name.

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