Support Vector Machines Based Arabic Language Text Classification System: Feature Selection Comparative Study

被引：25
|
作者：
Mesleh, Abdelwadood Moh'd [1 ]
机构：
[1] Balqa Appl Univ, Dept Comp Engn, Fac Engn Technol, Amman, Jordan
来源：
ADVANCES IN COMPUTER AND INFORMATIOM SCIENCES AND ENGINEERING | 2008年
关键词：
D O I：
10.1007/978-1-4020-8741-7_3
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
feature selection (FS) is essential for effective and more accurate text classification (TC) systems. This paper investigates the effectiveness of five commonly used FS methods for our Arabic language TC System. Evaluation used an in-house collected Arabic TC corpus. The experimental results are presented in terms of macro-averaging precision, macro-averaging recall and macro-averaging F-1 measure.
引用
收藏
页码：11 / 16
页数：6
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