Expected likelihood based query for active learning of Gaussian mixture models based classifiers

Abstract

Typically, a supervised machine learning requires sufficient labelled data to achieve satisfying performance. In many domains, however, the labelled data are often expensive to obtain and requires laborious human effort. In order to minimize the labelled data, we propose an active learning method by introducing the expected likelihood as a criterion in the query system, especially for the Gaussian mixture models based classifiers. In this scenario, the expected log-likelihood is calculated for all unlabelled examples in a pool data set, then an instant with the lowest value is chosen to query the object of the label. Experiments were performed to evaluate the effectiveness of the method. Empirically, using the two class artificial data set we observed that the proposed method can reduce significantly the number of labelled examples required to achieve a certain performance compared with passive learning.