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
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;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2013 International Conference on
Conference_Location :
Tianjin
DOI :
10.1109/ICMLC.2013.6890454
