Support vector machine text classifier for Arabic articles : ant colony optimization based feature subset selection
Dissertant
Muslih, Abd al-wadud Muhammad Abd al-Wadud
Thesis advisor
Comitee Members
al-Shalabi, Riyad
Abu Shawir, Bayan
al-Arif, Taha
University
Arab Academy for Financial and Banking Sciences
Faculty
The Faculty of Information Systems and Technology
Department
Computer information systems
University Country
Jordan
Degree
Ph.D.
Degree Date
2008
English Abstract
In this thesis, we have implemented a support vector machine (SVM) text classifier for Arabic articles.
Experimental results show that the SVM classifier outperformed Naive Bayesian (NB) and k-nearest neighbor (kNN) classifiers.
We investigated the effectiveness of six state-of-the-art feature subset selection (FSS) methods, which are commonly used in text classification (TC) tasks, for our Arabic SVM text classification system.
We implemented an Ant Colony Optimization Based-Feature Subset Selection (ACO Based-FSS) method for our Arabic SVM text classifier.
The proposed FSS method adapted Chi-square statistic as heuristic information and the effectiveness of the SVM classifier as a guide to improving the selection of features for each category.
Compared to the six state-of-the-art FSS methods, our ACO Based-FSS algorithm achieved better TC effectiveness.
Evaluation used an in-house Arabic TC corpus1 that consists of 1445 documents independently classified into nine categories.
The experimental results were presented in terms of macro-averaging precision, macro averaging recall and macro-averaging F1 measures.
Main Subjects
Information Technology and Computer Science
Topics
No. of Pages
114
Table of Contents
Table of contents.
Abstract.
Chapter one : Introduction.
Chapter two : Related work.
Chapter three : Text classification.
Chapter four : Feature subset selection.
Chapter five : Introduction to Arabic language.
Chapter six : Ant colony optimization based-feature subset selection algorithm for text classification tasks.
Chapter seven : Experimental results.
Chapter eight : Conclusions and future directions.
References.
American Psychological Association (APA)
Muslih, Abd al-wadud Muhammad Abd al-Wadud. (2008). Support vector machine text classifier for Arabic articles : ant colony optimization based feature subset selection. (Doctoral dissertations Theses and Dissertations Master). Arab Academy for Financial and Banking Sciences, Jordan
https://search.emarefa.net/detail/BIM-306127
Modern Language Association (MLA)
Muslih, Abd al-wadud Muhammad Abd al-Wadud. Support vector machine text classifier for Arabic articles : ant colony optimization based feature subset selection. (Doctoral dissertations Theses and Dissertations Master). Arab Academy for Financial and Banking Sciences. (2008).
https://search.emarefa.net/detail/BIM-306127
American Medical Association (AMA)
Muslih, Abd al-wadud Muhammad Abd al-Wadud. (2008). Support vector machine text classifier for Arabic articles : ant colony optimization based feature subset selection. (Doctoral dissertations Theses and Dissertations Master). Arab Academy for Financial and Banking Sciences, Jordan
https://search.emarefa.net/detail/BIM-306127
Language
English
Data Type
Arab Theses
Record ID
BIM-306127
