A Mutual Information based Feature Selection Algorithm

摘要：

The objective of the eliminating process is to reduce the size of the input feature set and at the same time to retain the class discriminatory information. This paper proposes and evaluates a new feature selection algorithm using information theory which is the mutual information (Ml) between combinations of input features and the class instead of mutual information between a single input feature and the class for both continuous-valued and discrete-valued features. Comparison studies of new and previously published classification algorithms indicate that the proposed algorithm is robust, stable and efficient.

关键词： feature ranking optimal feature set mutual information and classification

作者: Shuang Cang

作者单位: School of Tourism, Bournemouth University Poole, Dorset, BH12 5BB, UK

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: 1000-1004

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）