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| Optimizing Feature Selection Method for Text Classification with Shuffled Frog Leaping Algorithm |
Lu Yonghe( ), Chen Jinghuang |
| School of Information Management, Sun Yat-Sen University, Guangzhou 510006, China |
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Abstract [Objective]This paper introduces the shuffled frog leaping algorithm (SFLA) to remove the irrelevant terms from the texts, and optimizes the feature selection method to improve the accuracy of text classification. [Methods] First, we used CHI and IG techniques to pre-select different dimensions of feature terms, and then adopted the modified SFLA to refine the text features’ list. Second, we used a frog to represent a feature selection rule, and applied the classification precision as the fitness function. Finally, the SVM and KNN classifier were adopted to calculate the classification precision. [Results] The modified SFLA had better performance in classification precision than CHI and IG, and the highest increasing rate was 12%. [Limitations] The feature over fitting occured in small portion of space dimensions. [Conclusions] Using feature preselection and the modified SFLA could effectively exclude irrelevant or invalid terms, and then improve the precision of feature selection.
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Received: 30 September 2016
Published: 22 February 2017
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