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| Text Feature Selection Method Based on Particle Swarm Optimization |
| Lu Yonghe, Cao Lichao |
| School of Information Management, Sun Yat-Sen University, Guangzhou 510006, China |
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Abstract From the perspective of the overall impact of text features on the result of text categorization, a text feature selection method based on particle swarm optimization (PSOTFS)is proposed; to mine the text feature selection rules by PSO algorithm. At first, PSOTFS uses CHI to preselect the text features, then uses PSO algorithm to precisely select the text features from the preselected text features. PSOTFS uses a particle to represent a feature selection rule and the set of feature selection rules corresponds with a particle swarm. At the same time, the classification precision is used as the fitness function and grouping is used to reduce the dimensions of the particles. The experiment result shows that the text categorization effectiveness of PSOTFS is better than that of CHI, information gain, document frequency and mutual information.
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Received: 04 May 2011
Published: 09 October 2011
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