Title :
An Improved Maximum Relevance and Minimum Redundancy Feature Selection Algorithm Based on Normalized Mutual Information
Author :
Vinh, La The ; Thang, Nguyen Duc ; Lee, Young-Koo
Author_Institution :
Dept. of Comput. Eng., Kyung Hee Univ., Yongin, South Korea
Abstract :
We present in this paper a comprehensive analysis of the mutual information based feature selection algorithms. We point out the limitations of some recent work in this area then propose an improvement to overcome the weak points. The experiment results confirm that we achieve a better feature sets compared with the two recent developed algorithms, which are Maximum Relevance and Minimum Redundancy (mRMR) and Normalized Mutual Information Feature Selection (NMIFS), in terms of the classification accuracy.
Keywords :
feature extraction; pattern classification; feature sets; improved maximum relevance; minimum redundancy feature selection algorithm; normalized mutual information feature selection; Accuracy; Computers; Electronic mail; Feature extraction; Mutual information; Support vector machines; Vehicles; feature selection; max relevance; min redundance; mutual information;
Conference_Titel :
Applications and the Internet (SAINT), 2010 10th IEEE/IPSJ International Symposium on
Conference_Location :
Seoul
Print_ISBN :
978-1-4244-7526-1
Electronic_ISBN :
978-0-7695-4107-5
DOI :
10.1109/SAINT.2010.50
