An Improved LAM Feature Selection Algorithm

摘要：

In text categorization, feature selection is an effective feature dimension-reduction methods. To solve the problems of unadaptable high original feature space dimension, too much irrelevance, data redundancy and difficulties in selecting a threshold, we propose an improved LAM feature selection algorithm (ILAMFS). Firstly, combining the gold segmentation and the LAM algorithm based on the characteristics and the category of the correlation analysis, filtering the original feature set, and retaining the feature selection with strong correlation and weak category. Secondly, with the improved LAM algorithm, weighted average and Jaccard coefficient of such thoughts feature subsets make redundancy filtering out redundant features. Finally, we obtain an approximate optimal feature subset. Experimental results show that this method is effective in data dimension on reduction, threshold selection and furthermore, in reducing the computation amount and precision in the feature selection.

关键词： Features Selection Correlation Redundancy Weighted A verage

作者: Yong-gong Ren Nan Lin Yu-qi Sun

作者单位: School of Computer and Information Technology, Liaoning Normal University Dalian, China

会议类型: 国际会议

会议名称: 2010 Seventh Web Information System and Applications Conference(第七届全国web信息系统及其应用学术会议)

会议地点: 呼和浩特

会议语种:英文

页码: 35-38

在线出版日期: 2010-08-20（万方平台首次上网日期，不代表论文的发表时间）