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