JOURNAL ARTICLE

An Efficient Feature Selection Method for Arabic Text Classification

Bilal HawashinAyman MansourShadi Aljawarneh

Year: 2013 Journal:   International Journal of Computer Applications Vol: 83 (17)Pages: 1-6

Abstract

This paper proposes an efficient, Chi-Square-based, feature selection method for Arabic text classification.In Data Mining, feature selection is a preprocessing step that can improve the classification performance.Although few works have studied the effect of feature selection methods on Arabic text classification, limited number of methods was compared.Furthermore, different datasets were used by different works.This paper improves the previous works in three aspects.First, it proposes a new efficient feature selection method for enhancing Arabic text classification.Second, it compares extended number of existing feature selection methods.Third, it adopts two publicly available datasets to encourage future works to adopt them in order to guarantee fair comparisons among the various works.Our experiments show that our proposed method outperformed the existing methods in term of accuracy.

Keywords:
Computer science Arabic Feature selection Selection (genetic algorithm) Feature (linguistics) Artificial intelligence Natural language processing Pattern recognition (psychology) Information retrieval Linguistics

Metrics

32
Cited By
3.77
FWCI (Field Weighted Citation Impact)
19
Refs
0.94
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Text and Document Classification Technologies
Physical Sciences →  Computer Science →  Artificial Intelligence
Spam and Phishing Detection
Physical Sciences →  Computer Science →  Information Systems
Imbalanced Data Classification Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence

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