A new feature selection algorithm based on Mutual Information with pairwise constraints

摘要：

Feature selection plays an important role in the area of machine learning. Class Label is often used as the supervised information for supervrsed feature selection algorithm while constraints are rarely used. So, an effective feature selection algorithm with pairwise constraints called Constraints Score was proposed. But its performance still is limited by neglecting the correlation between features. In this paper we improve this algorithm by considering the correlation between features and using SVM density estimation, mutual information to measure the correlation and further eliminate the feature redundancy. Experiments show the effectiveness of our improved algorithm.

关键词： semi-feature selection mutual information SVM density estimation

作者: Song Jing Yang Ming Ji Genlin Cai Wenbin

作者单位: School of Computer Science and Technology, Nanjing Normal University Nanjing 210097, P.R.China Jiangsu Research Center of Information Security & Privacy Technology, Nanjing 210097,P.R China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 483-486

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）