Text Categorization Model Based on Fusing of Gray Theory and Information Gain

LIU Hai-feng; LI Kai-qi; WANG Ze-yan

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Text Categorization Model Based on Fusing of Gray Theory and Information Gain

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- Text Categorization Model Based on Fusing of Gray Theory and Information Gain
- Vol. 29, Issue 10, Pages: 94-98(2012)
- 作者机构：
  
  1. 解放军理工大学理学院,江苏,南京,210007
  2. 解放军理工大学指挥自动化学院,江苏,南京,210007
- 作者简介：
- 基金信息：
- DOI：
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- Published：2012
- 稿件说明：
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刘海峰, 李凯齐, 汪泽焱. 基于灰关系与信息增益融合的文本分类模型[J]. 微电子学与计算机, 2012, 29(10): 94-98.

LIU Hai-feng, LI Kai-qi, WANG Ze-yan. Text Categorization Model Based on Fusing of Gray Theory and Information Gain[J]. 2012, 29(10): 94-98.
刘海峰, 李凯齐, 汪泽焱. 基于灰关系与信息增益融合的文本分类模型[J]. 微电子学与计算机, 2012, 29(10): 94-98. DOI：

LIU Hai-feng, LI Kai-qi, WANG Ze-yan. Text Categorization Model Based on Fusing of Gray Theory and Information Gain[J]. 2012, 29(10): 94-98. DOI：

摘要

针对信息增益模型在文本分类中的不足之处

提出了一种基于灰关系与信息增益的文本分类算法.首先基于改进的χ2统计进行类别特征选择用于类内文本表示

提高类别中心向量的表示能力;其次针对IG模型对低频词赋权过大问题

提出了基于频数和位置的改进加权方法;最后提出了基于灰关系的文本相似度计算途径

改善了基于距离的相似度计算模式的不足.试验表明

此算法提高了文本分类效率.

Abstract

In view of the information gain model defects in the text classification

this article puts forward a text classification algorithm based on the grey relation and information gain. Firstly

we improved a method of x2 statistics in sort feature selection in order to express text. In this way

we improve the precision of the class center vector. Secondly

according to the IG model weights the low frequency words too bigger

we put forward an improved weighted method basing on frequency and position. Lastly

we put forward a new way in text similarity calculation in order to improve the shortcomings of the similarity calculation model that based on distance. Subsequent text categorization test shows that this paper puts forward an improved IG method and enhances the text classification efficiency.

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