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Title: Greedy active learning algorithm for logistic regression models Authors:  Ray-Bing Chen - National Cheng Kung University (Taiwan) [presenting]
Abstract: A logistic model-based active learning procedure for binary classification problems is studied, 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, 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 to confirm the performance of our method.