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论文类型：期刊论文

发表时间：2018-09-01

发表刊物：CHINESE JOURNAL OF ELECTRONICS

收录刊物：SCIE

卷号：27

期号：5

页面范围：1015-1024

ISSN号：1022-4653

关键字：Graph-based semi-supervised classification (GSSC); Cost-sensitive; Rescale

摘要：Assuming that misclassification costs between different categories are equal, traditional Graph based semi-supervised classification (GSSC) algorithms pursues high classification accuracy. In many practical problems, especially in the fields of finance and medicine, compared with global classification accuracy, less cost on global misclassification is more likely to be the most significant factor. We propose one novel cost-sensitive classification algorithm based on the local and global consistency, which utilizes the semi-supervised classification algorithms better, and ensures higher classification accuracy on the basis of reducing overall cost. Our improved algorithm may bring some problems due to unbalanced data account, so we introduce synthetic minority oversampling technique algorithm for further optimization. Experimental results of bank loans and medical problems verify the effectiveness of our novel classification algorithm.