Semi-supervised learning in knowledge discovery

Recently, semi-supervised learning has received quite a lot of attention. The idea of semi-supervised learning is to learn not only from the labeled training data, but to exploit also the structural information in additionally available unlabeled data. In this paper we review existing semi-supervised approaches, and propose an evolutionary algorithm suited to learn interpretable fuzzy if-then classification rules from partially labeled data. Feasibility of our approach is shown on artificial datasets, as well as on a real-world image analysis application.