Feature sub-set selection metrics for Arabic text classification

被引：49

作者：

Mesleh, Abdelwadood Moh'd ^{[1
]}

机构：

[1] Al Blaqa Appl Univ, Fac Engn Technol, Dept Comp Engn, Amman, Jordan

来源：

PATTERN RECOGNITION LETTERS | 2011年 / 32卷 / 14期

关键词：

Feature selection; SVM; Arabic text classification; MODEL; KNN;

D O I：

10.1016/j.patrec.2011.07.010

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Feature sub-set selection (FSS) is an important step for effective text classification (TC) systems. This paper presents an empirical comparison of seventeen traditional FSS metrics for TC tasks. The TC is restricted to support vector machine (SVM) classifier and only for Arabic articles. Evaluation used a corpus that consists of 7842 documents independently classified into ten categories. The experimental results are presented in terms of macro-averaging precision, macro-averaging recall and macro-averaging F-1 measures. Results reveal that Chi-square and Fallout FSS metrics work best for Arabic TC tasks. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：1922 / 1929

页数：8

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