Impact of Fuzziness Measures on the Performance of Semi-supervised Learning

Abstract

Usage of fuzziness in the study of semi-supervised learning is relatively new. In this study, the divide-and-conquer strategy is used to investigate the performance of semi-supervised learning. To this end, testing dataset is divided into three categories, namely low, medium and high-fuzzy samples based on the magnitude of fuzziness of each sample. It is experimentally confirmed that if the low-fuzzy samples are added from the testing dataset to the original training dataset and the model is retrained, then the accuracy can be improved. To measure the amount of fuzziness of each sample, four different fuzziness measuring models are used in this study. Experimental results support that improvement of accuracy is dependent on which fuzziness measuring model is used to measure the fuzziness of each sample. Wilcoxon signed-rank test shows that choosing a specific fuzziness measuring model is significant or not. Finally, from the Wilcoxon signed-rank test, the best model is chosen, which can be used along with semi-supervised learning to improve its performance.