DocumentCode :
3759438
Title :
Improved Feature Selection Based on Normalized Mutual Information
Author :
Li Yin;Ma Xingfei;Yang Mengxi;Zhao Wei;Gu Wenqiang
Author_Institution :
Educ. Inf. Res. Center, WuXi Vocation Inst. of Commerce, Wuxi, China
fYear :
2015
Firstpage :
518
Lastpage :
522
Abstract :
For the question (NMIFS) algorithm has the disadvantages of redundancy. This paper introduces a new feature selection method by enhanced NMIFS algorithm. A new quality estimation function is introduced in the new feature selection algorithm to overcome the shortcomings of the classic NMIFS, and the experiment shows on that normalized mutual information feature selection The experiment shows that the INMIFS can generate impressive results in accuracy and redundancy.
Keywords :
"Mutual information","Redundancy","Classification algorithms","Entropy","Training","Bayes methods","Decision trees"
Publisher :
ieee
Conference_Titel :
Distributed Computing and Applications for Business Engineering and Science (DCABES), 2015 14th International Symposium on
Type :
conf
DOI :
10.1109/DCABES.2015.135
Filename :
7429669
Link To Document :
بازگشت