Semi-supervised Fuzzy Learning in Text Categorization

摘要：

Semi-supervised learning has attracted a lot of attention in recent years. Different from traditional supervised learning. Semi-supervised learning makes use of both labeled and unlabeled samples. In test categorization, traditional classifier prefer lots of samples and each category have same number of simples, but collecting labeled examples costs human efforts and certain category may be cant find abundance samples; this situation would lead to low classification accuracy. In this paper, we proposed a semi-supervised test classifier based on fuzzy cmeans algorithm. Experiment show that our method has better performance in small samples and unbalance samples.

关键词： Semi-supervised Fuzzy C-Means Text Categorization Unlabeled samples

作者: Xin Pan Suli Zhang

作者单位: School of Electrical & Information Technology Changchun Institute of Technology Changchun, China

会议类型: 国际会议

会议名称: 2011 Eighth International Conference on Fuzzy System and Knowledge Discovery(第八届模糊系统与知识发现国际会议 FSKD 2011)

会议地点: 上海

会议语种:英文

页码: 1107-1109

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）