Active Learning with Committees and the Selection of Starting Sets

Abstract

Obtaining tagged training data takes a long time and also is a costly task. Active learning aims machine learning algorithms achieve reasonable accuracies with less tagged training data. To this purpose, one of the methods for determining which samples to be tagged is making use of the decisions of classifier ensembles. Within this work, we implemented a committee-based active learning application and compared it with non-active methods.