DocumentCode :
3032557
Title :
Effective feature selection with Particle Swarm Optimization based one-dimension searching
Author :
Wang, Jun ; Zhao, Yan ; Liu, Ping
Author_Institution :
Dept. of Electron. Eng., Shantou Univ., Shantou, China
fYear :
2010
fDate :
8-10 June 2010
Firstpage :
702
Lastpage :
705
Abstract :
Forming an efficient feature space for classification problems is a grand challenge in pattern recognition. Many optimization algorithms are adopted to do feature selection, but these algorithms do searching in multi-dimensions space and always cannot get the optimal feature subset. In this paper, a feature selection method with Particle Swarm Optimization based one-dimension searching is proposed to improve the classification performance. Experimental results show that the proposed method can do feature selection more effectively than the compared method and get much higher classification accuracy.
Keywords :
particle swarm optimisation; pattern classification; classification problems; feature selection; one-dimension searching method; particle swarm optimization; pattern recognition; Classification algorithms; Complexity theory; Gallium; Kernel; Machine learning algorithms; Particle swarm optimization; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems and Control in Aeronautics and Astronautics (ISSCAA), 2010 3rd International Symposium on
Conference_Location :
Harbin
Print_ISBN :
978-1-4244-6043-4
Electronic_ISBN :
978-1-4244-7505-6
Type :
conf
DOI :
10.1109/ISSCAA.2010.5632559
Filename :
5632559
Link To Document :
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