Simply recycled selection and incrementally reinforced selection methods applicable for semi-supervised learning algorithms

Abstract

This paper presents an empirical study on selecting a small amount useful unlabeled data with which the classification accuracy of semi-supervised learning (SSL) algorithms can be improved. In particular, two selection strategies, named simply recycled selection and incrementally reinforced selection, are considered and empirically compared. The experimental results, obtained with well-known benchmark data sets, demonstrate that the latter works better than the former does in terms of classification accuracy.