A hybrid selection method of helpful unlabeled data applicable for semi-supervised learning algorithms

This paper presents an empirical study on selecting a small amount of useful unlabeled data with which the classification accuracy of semi-supervised learning algorithms can be improved. In particular, a hybrid method of unifying the simply recycled selection method and the incrementally reinforced selection method is considered and empirically evaluated. The experimental results, obtained using well-known benchmark data sets through semi-supervised support vector machines, demonstrate that the hybrid method works better than the traditional ones do in terms of classification accuracy.