DocumentCode :
3569095
Title :
Feature selection based on modified minimize entropy principle
Author :
Chen, Shian, Jr. ; Chou, Hung-Lieh ; Tai, David Wen-Shung
Author_Institution :
Dept. of Comput. Sci. & Inf. Manage., HUNGKUANG Univ., Taichung, Taiwan
Volume :
1
fYear :
2010
Abstract :
Feature selections have seen growing importance placed on statistics, pattern recognition, machine learning and data mining. Researchers have demonstrated the interest in the methods for improving the performance of their forecasting results. Therefore, this study proposes a feature selection approach, which based on minimize entropy principle approach. Experimental results have shown that the proposed model provided more average accuracy rate and stability then other methods.
Keywords :
data mining; entropy; learning (artificial intelligence); pattern recognition; data mining; feature selection; machine learning; modified minimize entropy principle; pattern recognition; Accuracy; Classification algorithms; Computer science; Data mining; Entropy; Machine learning; Windows; Feature Selection; Minimize Entropy Principle;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electronics and Information Engineering (ICEIE), 2010 International Conference On
Print_ISBN :
978-1-4244-7679-4
Electronic_ISBN :
978-1-4244-7681-7
Type :
conf
DOI :
10.1109/ICEIE.2010.5559828
Filename :
5559828
Link To Document :
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