Text Categorization Method Based on Improved Mutual Information and Characteristic Weights Evaluation Algorithms

被引：1
|
作者：
Pei, Zhili [1 ,2 ]
Shi, Xiaohu [1 ]
Marchese, Maurizio [3 ]
Liang, Yanchun [1 ,3 ]
机构：
[1] Jilin Univ, Coll Comp Sci & Technol, Key Lab Symbol Comp & Knowledge Engn, Minist Educ, Changchun, Peoples R China
[2] Natl Univ Inner Mongolia, Coll Math & Comp Sci, Tongliao 028043, Peoples R China
[3] Univ Trent, Dept Informat & Commun Technol, I-38050 Trento, Italy
来源：
FOURTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 4, PROCEEDINGS | 2007年
基金：
美国国家科学基金会;
关键词：
D O I：
10.1109/FSKD.2007.559
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The improvement of text categorization by statistical methods can be performed from two main directions, namely the feature selection and the evaluation of characteristic weights. In this paper, we propose an enhanced text categorization method based on a modified mutual information algorithm and evaluation algorithm of characteristic weights which improves both aspects. The proposed method is applied to the benchmark test set Reuters-21578 Top10 to examine its effectiveness. Numerical results show that the precision, the recall and the value of F1 of the proposed method are all superior tothose of existing conventional methods.
引用
收藏
页码：87 / +
页数：2
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