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Study on Feature Selection Algorithm Based on Least Squares Support Vector Machine and Particle Swarm Optimization

Author: SongChuYi
Tutor: LiangYanChun´╝ŤWuChunGuo
School: Jilin University
Course: Software Engineering
Keywords: Feature selection Least squares support vector machine Particle swarm optimization
Type: Master's thesis
Year: 2008
Downloads: 360
Quote: 2
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Classification problem is a major problem in the field of pattern recognition , classification accuracy classifier selection, number of samples , and sample dimension has a great relationship . Which samples of feature selection problem is the first encounter of the key issues , the extracted classification useful and efficient features are particularly important . With the improvement of scientific and technological level , the sample of people in some areas have higher dimension , therefore , feature selection problem is getting people's attention . Many researchers dedicated to the study of the problem of feature selection . In recent years , bio - intelligence - based evolutionary algorithm has been fully developed, and at the same time , based on intelligent algorithm and its hybrid algorithm feature selection methods are emerging . Particle swarm optimization and least squares support vector machines , improved speed and position of discrete particle swarm algorithm update formula , proposed a hybrid feature selection method . This method makes full use of the advantages of least squares support vector machine easily solved , made ??using least squares support vector machine classifier , and the main component of the classification accuracy as the fitness function of the particle swarm algorithm optimization process . The experiments show that the proposed method can be effectively obtain larger characteristics contribute to the classification , thereby reducing the number of data dimensions , to improve the classification efficiency .

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory
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