Greedy Active Learning Algorithm for Logistic Regression Models

• 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
• 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

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 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.