DocumentCode
693145
Title
A method for feature selection based on the optimal hyperplane of SVM and independent analysis
Author
Lin-Fang Hu ; Wei Gong ; Li-Xiao Qi ; Ping Wang
Author_Institution
Sch. of Control & Mech. Eng., Tianjin Inst. of Urban Constr., Tianjin, China
Volume
01
fYear
2013
fDate
14-17 July 2013
Firstpage
114
Lastpage
117
Abstract
Feature selection is an important topic in machine learning. In order to evaluate the candidate features, a strategy based on the constituent principle of the SVM optimal hyperplane is established in this paper. Then, by considering different feature combinations, a better feature subset can be obtained. The method is used to recognize the monomers in weather forecast, and experimental results demonstrate its effectiveness in enhancing the classification performance.
Keywords
learning (artificial intelligence); molecules; support vector machines; weather forecasting; SVM optimal hyperplane; feature selection; feature subset; independent analysis; machine learning; monomers; weather forecast; Abstracts; Support vector machines; Correlation analysis; Feature Selection; Support vector machine; The optimal hyperplane;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics (ICMLC), 2013 International Conference on
Conference_Location
Tianjin
Type
conf
DOI
10.1109/ICMLC.2013.6890454
Filename
6890454
Link To Document