An Improved Feature Selection Algorithm in Text Classification

CHEN Jian-hua; WANG Zhi-he; JIANG Yun; XU Hu-yin; FAN Dong-hui

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An Improved Feature Selection Algorithm in Text Classification

更新时间：2026-09-04

- An Improved Feature Selection Algorithm in Text Classification
- Vol. 28, Issue 12, Pages: 180-183(2011)
- 作者机构：
  
  西北师范大学机构数学与信息科学学院,甘肃,兰州,730070
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2011
- 稿件说明：
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陈建华, 王治和, 蒋芸, 许虎寅, 樊东辉. 一种改进的文本分类特征选择算法[J]. 微电子学与计算机, 2011, 28(12): 180-183.

CHEN Jian-hua, WANG Zhi-he, JIANG Yun, et al. An Improved Feature Selection Algorithm in Text Classification[J]. 2011, 28(12): 180-183.
陈建华, 王治和, 蒋芸, 许虎寅, 樊东辉. 一种改进的文本分类特征选择算法[J]. 微电子学与计算机, 2011, 28(12): 180-183. DOI：

CHEN Jian-hua, WANG Zhi-he, JIANG Yun, et al. An Improved Feature Selection Algorithm in Text Classification[J]. 2011, 28(12): 180-183. DOI：

摘要

现有的一种特征选择算法DPM(Discriminating Power Measure)1

是通过计算每个特征在某一类别和剩余其他类别中的文档频

比较了特征对一个类别和对其他类别的贡献

提取出具有强类别区分能力的特征词.在研究此特征选择算法的基础上

提出了一种改进的特征选择算法

该算法同时考虑了每个特征的类别频次在计算特征类别区分能力方面的重要性.经实验验证

改进后的特征选择算法能够获得较好的分类效果.

Abstract

By calculating the document frequency of each feature in a category and other categories

an existing feature selection algorithm-DPM (Discriminating Power Measure) compares the contribution of features for one category with other categories

and extracts the features that reveal larger differences among categories.On the basis of the research of DPM

a new feature selection algorithm is proposed

which considers the importance of word frequency of each feature in the calculation of discriminating power of features.Experiments demonstrate that the proposed algorithm has a better performance.

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