Semi-supervised Fuzzy Min–Max Neural Network for Data Classification

Abstract

Learning from the lack of labeled data is a challenging task which often limits the performance of the classifier. Since the unlabeled data is easy to obtain, using both of the labeled and unlabeled data in the training process provide a way to solve this problem. In this paper, a semi-supervised classification method based on fuzzy min–max neural network (SS-FMM) is proposed. In SS-FMM, the network has been modified for handling both of the labeled and unlabeled data. In addition, the staged feedback process is designed to modify the network structure of the traditional fuzzy min–max neural network. A staged-threshold function designed in SS-FMM, the hyperbox pruning process and the hyperbox relabeling process can be started dynamically. Moreover, the hyperboxes relabeling process and the hyperbox pruning process are designed to maximize using the unlabeled data and control the amount of the hyperboxes. In order to testify the effectiveness of SS-FMM, various experiments are carried out with several benchmark data sets. In addition, SS-FMM has been applied on the internal inspection data of our system. The results show that SS-FMMM has got good performance.