Feature selection method based on the improved of mutual information and genetic algorithm

Y. Qiu, Peiyu Liu, Yuzhen Yang
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Abstract

The feature selection is a key method of text categorization technology, this paper proposed a text feature selection method based on the improved of mutual information and genetic algorithm. Used the improved of mutual information algorithm to do the initial choose to removing redundancy and noise words at first, and then used the genetic algorithm to training the template which generate by a subset of words, so get the optimal feature subset that on behalf of the issue space, to achieve dimensionality reduction and improved classification accuracy.
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基于互信息改进和遗传算法的特征选择方法
特征选择是文本分类技术的关键方法,本文提出了一种基于互信息改进和遗传算法的文本特征选择方法。首先利用改进的互信息算法对去除冗余和噪声词进行初始选择,然后利用遗传算法对词子集生成的模板进行训练,得到代表问题空间的最优特征子集,实现降维,提高分类精度。
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