Performance of KNN and SVM classifiers on full word Arabic articles

被引：76

作者：

Hmeidi, Ismail ^{[1
]}

Hawashin, Bilal ^{[1
]}

El-Qawasmeh, Eyas ^{[1
]}

机构：

[1] Jordan Univ Sci & Technol, Fac Comp & Informat Technol, Irbid 22110, Jordan

来源：

ADVANCED ENGINEERING INFORMATICS | 2008年 / 22卷 / 01期

关键词：

Arabic text categorization; full word features; tf.idf weighting; CHI statistics; KNN; SVM;

D O I：

10.1016/j.aei.2007.12.001

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper reports a comparative study of two machine learning methods on Arabic text categorization. Based on a collection of news articles as a training set, and another set of news articles as a testing set, we evaluated K nearest neighbor (KNN) algorithm, and support vector machines (SVM) algorithm. We used the full word features and considered the tf.idf as the weighting method for feature selection, and CHI statistics as a ranking metric. Experiments showed that both methods were of superior performance on the test corpus while SVM showed a better micro average F1 and prediction time. (C) 2007 Elsevier Ltd. All rights reserved.

引用

页码：106 / 111

页数：6

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