Semi-supervised Fuzzy Relational Classifier

Recently, semi-supervised learning has attracted much attention, and it is applicable to both clustering and classification. But among a large number of these algorithms, only a few considered combining semi-supervised clustering and classification together. However, in supervised learning, the fuzzy relational classifier (FRC) is a recently proposed two-step nonlinear classifier which combines the unsupervised clustering and supervised classification together. Inspired from FRC, in this paper, we present a new method, called Semi-supervised Fuzzy Relational Classifier (SSFRC), which combines semi-supervised clustering and classification together. In the proposed SSFRC, we employ the semi-supervised pair wise-constrained competitive agglomeration (PCCA) to replace FCM to obtain clusters fitting user expectations without specifying the exact cluster number. In addition, we incorporate the fuzzy class labels of unlabeled data into the classification mechanism to improve its performance. The experimental results on real-life datasets demonstrate that SSFRC can outperform FRC with all data labeled in classification performance.