关键词: 可能性聚类, 半监督分类, 隶属度函数, 局部加权均值, 模糊熵

Abstract:

Graph based Semi-Supervised Learning（GSSL） has been attracting more and more attention with its intuitiveness and good learning performance in the machine learning community. The existing GSSL method constructs undirected weight graph, which is sensitive or not enough robust to noise or abnormal data. To handle the problem, a Semi-Supervised Classification Method of Possibilistic Clustering Assumption（SSPCA） is proposed. Then, for improving the reliability of the classification method, the SSPCA constrains each instance to have the same label membership value as its local weighted mean. A regularization term about fuzzy entropy that is added into the objective function. It aims to increase the data discriminated information. After that, the resulting label membership function has more generalization and the robust of the proposed method is improved on the noise and abnormal data. Some real world problems implementation on the basis of the proposed SSPCA show their reliability and robustness classification.

Key words: probability clustering, semi-supervised classification, membership function, local weighted mean, fuzzy entropy