Feature selection method based on statistics of compound words for Arabic text classification
Joint Authors
Adil, Aishah
al-Barid, Muhammad
al-Shabbi, Adil
Umar, Nazliya
Source
The International Arab Journal of Information Technology
Issue
Vol. 16, Issue 2 (31 Mar. 2019), pp.178-185, 8 p.
Publisher
Publication Date
2019-03-31
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
One of the main problems of text classification is the high dimensionality of the feature space.
Feature selection methods are normally used to reduce the dimensionality of datasets to improve the performance of the classification, or to reduce the processing time, or both.
To improve the performance of text classification, a feature selection algorithm is presented, based on terminology extracted from the statistics of compound words, to reduce the high dimensionality of the feature space.
The proposed method is evaluated as a standalone method and in combination with other feature selection methods (two-stage method).
The performance of the proposed algorithm is compared to the performance of six well-known feature selection methods including Information Gain, Chi-Square, Gini Index, Support Vector Machine-Based, Principal Components Analysis and Symmetric Uncertainty.
A wide range of comparative experiments were conducted on three Arabic standard datasets and with three classification algorithms.
The experimental results clearly show the superiority of the proposed method in both cases as a standalone or in a two-stage scenario.
The results show that the proposed method behaves better than traditional approaches in terms of classification accuracy with a 6-10 % gain in the macro-average, F1.
American Psychological Association (APA)
Adil, Aishah& al-Barid, Muhammad& Umar, Nazliya& al-Shabbi, Adil. 2019. Feature selection method based on statistics of compound words for Arabic text classification. The International Arab Journal of Information Technology،Vol. 16, no. 2, pp.178-185.
https://search.emarefa.net/detail/BIM-894971
Modern Language Association (MLA)
Adil, Aishah…[et al.]. Feature selection method based on statistics of compound words for Arabic text classification. The International Arab Journal of Information Technology Vol. 16, no. 2 (Mar. 2019), pp.178-185.
https://search.emarefa.net/detail/BIM-894971
American Medical Association (AMA)
Adil, Aishah& al-Barid, Muhammad& Umar, Nazliya& al-Shabbi, Adil. Feature selection method based on statistics of compound words for Arabic text classification. The International Arab Journal of Information Technology. 2019. Vol. 16, no. 2, pp.178-185.
https://search.emarefa.net/detail/BIM-894971
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 183-185
Record ID
BIM-894971