An improved active learning in unbalanced data classification

Abstract

This paper is concerned with the unbalanced classification problem which occurs when there are significantly less number of observations of the target concept. The standard machine learning algorithms yield better prediction performance with balanced datasets. However, in real application, it is quite common to have unbalanced dataset with a certain class of interest having very small size. It will be problematic since the algorithm might predict all the cases into majority classes without loss of overall accuracy. In this paper, we propose an efficient way of selecting informative for active learning which does not necessitate a search through the entire dataset and allows active learning to be applied to very large datasets. Experimental results show that the proposed method decreases the prediction error of minority class significantly with increasing the prediction error or majority class a little bit. © 2011 Springer-Verlag.