Adaptive Informative Sampling for Active Learning

Many approaches to active learning involve periodically training one classifier and choosing data points with the lowest confidence. An alternative approach is to periodically choose data instances that maximize disagreement among the label predictions across an ensemble of classifiers. Many classifiers with different underlying structures could fit this framework, but some ensembles are more suitable for some data sets than others. The question then arises as to how to find the most suitable ensemble for a given data set. In this work we introduce a method that begins with a heterogeneous ensemble composed of multiple instances of different classifier types, which we call adaptive informative sampling (AIS). The algorithm periodically adds data points to the training set, adapts the ratio of classifier types in the heterogeneous ensemble in favor of the better classifier type, and optimizes the classifiers in the ensemble using stochastic methods. Experimental results show that the proposed method performs consistently better than homogeneous ensembles. Comparison with random sampling and uncertainty sampling shows that the algorithm effectively draws informative data points for training.