Active Learning with Partially Labeled Data via Bias Reduction

Minoo Aminian and Ian Davidson, State University of New York, Albany

With active learning the learner participates in the process of selecting instances so as to speed up convergence to the "best" model. This paper presents a principled method of instance selection based on the recent bias variance decomposition work for a 0-1 loss function. We focus on bias reduction to reduce the 0-1 loss by using an approximation to the optimal Bayes classifier to calculate the bias for an instance. We have applied the proposed method to naive Bayes learning on a number of bench mark data sets showing that using this active learning approach decreases the generalization error at a faster rate than randomly adding instances and converges to the optimal Bayes classifier error obtained from the original data set.


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