An enhanced feature selection technique for classification of group-based holy quran verses

This thesis is about proposing an enhanced feature selection technique for text classification applications. Text classification problem is primarily applied in document labeling. However, the major setbacks with the existing feature selection techniques are high computational runtime associated wit...

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Main Author:	Oyekunle, Adeleke Abdullahi
Format:	Thesis
Language:	English English English
Published:	2018
Subjects:	HB Economic Theory
Online Access:	http://eprints.uthm.edu.my/7549/2/24p%20ADELEKE%20ABDULLAHI%20OYEKUNLE.pdf http://eprints.uthm.edu.my/7549/1/ADELEKE%20ABDULLAHI%20OYEKUNLE%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/7549/3/ADELEKE%20ABDULLAHI%20OYEKUNLE%20WATERMARK.pdf
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spelling	my-uthm-ep.75492022-08-21T01:44:33Z An enhanced feature selection technique for classification of group-based holy quran verses 2018-01 Oyekunle, Adeleke Abdullahi HB Economic Theory HB135-147 Mathematical economics. Quantitative methods. Including econometrics, input-output analysis, game theory This thesis is about proposing an enhanced feature selection technique for text classification applications. Text classification problem is primarily applied in document labeling. However, the major setbacks with the existing feature selection techniques are high computational runtime associated with wrapper-based FS techniques and low classification accuracy performance associated with filter-based FS techniques. Therefore, in this study, a hybrid feature selection technique is proposed. The proposed FS technique is a combination of JUter-based information gain (JG) and wrapper-based CFS algorithms. The purpose of combining these two FS algorithms is to achieve both high classification accuracy perfonnance (wrapper) at lower computational runtime (filter). The study also developed a group-based Quran dataset to improve on the understanding and analysis of the textual data (Quranic verses). The group-based dataset is a combination of Holy Quran translation and commentary (tafsir). The Quranic verses were selected from two chapters, Surah AlBaqarah and Surah Al-Anaam. The verses are classified into three categories: Faith, Worship, and Etiquette. In the experiment, six feature selection algorithms were applied: In.formation Gain (JG), Chi-square (CH), Pearson Correlation Coefficient (PCC), RelieJF, Correlation-based (CFS), and the proposed JG-CFS algorithms. The textual data (Quranic verses) were preprocessed using StringtoWordVector with weighted Term Frequency-Inverse Document Frequency (IF-IDF). Meanwhile, the classification phase has involved four algorithms: Nai've Bayes (NB), k-Nearest Neighbor (k-NN), Support Vector Machine (LibSVM), and Decision Trees (148). The experiment results were evaluated based on two established perfonnance metrics in text classification: Accuracy and Area under Receiver Operating Characteristics (ROC) curve (A UC). The proposed hybrid feature selection technique has shown promising results in tenns of Accuracy and Area under Receiver Operating Characteristics (ROC) curve (A UC) by achieving at a lower computational runtime (3.89secs) Accuracy of94.5% and AUC of0.944 with the group-based Quran dataset. 2018-01 Thesis http://eprints.uthm.edu.my/7549/ http://eprints.uthm.edu.my/7549/2/24p%20ADELEKE%20ABDULLAHI%20OYEKUNLE.pdf text en public http://eprints.uthm.edu.my/7549/1/ADELEKE%20ABDULLAHI%20OYEKUNLE%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/7549/3/ADELEKE%20ABDULLAHI%20OYEKUNLE%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	HB Economic Theory HB Economic Theory
spellingShingle	HB Economic Theory HB Economic Theory Oyekunle, Adeleke Abdullahi An enhanced feature selection technique for classification of group-based holy quran verses
description	This thesis is about proposing an enhanced feature selection technique for text classification applications. Text classification problem is primarily applied in document labeling. However, the major setbacks with the existing feature selection techniques are high computational runtime associated with wrapper-based FS techniques and low classification accuracy performance associated with filter-based FS techniques. Therefore, in this study, a hybrid feature selection technique is proposed. The proposed FS technique is a combination of JUter-based information gain (JG) and wrapper-based CFS algorithms. The purpose of combining these two FS algorithms is to achieve both high classification accuracy perfonnance (wrapper) at lower computational runtime (filter). The study also developed a group-based Quran dataset to improve on the understanding and analysis of the textual data (Quranic verses). The group-based dataset is a combination of Holy Quran translation and commentary (tafsir). The Quranic verses were selected from two chapters, Surah AlBaqarah and Surah Al-Anaam. The verses are classified into three categories: Faith, Worship, and Etiquette. In the experiment, six feature selection algorithms were applied: In.formation Gain (JG), Chi-square (CH), Pearson Correlation Coefficient (PCC), RelieJF, Correlation-based (CFS), and the proposed JG-CFS algorithms. The textual data (Quranic verses) were preprocessed using StringtoWordVector with weighted Term Frequency-Inverse Document Frequency (IF-IDF). Meanwhile, the classification phase has involved four algorithms: Nai've Bayes (NB), k-Nearest Neighbor (k-NN), Support Vector Machine (LibSVM), and Decision Trees (148). The experiment results were evaluated based on two established perfonnance metrics in text classification: Accuracy and Area under Receiver Operating Characteristics (ROC) curve (A UC). The proposed hybrid feature selection technique has shown promising results in tenns of Accuracy and Area under Receiver Operating Characteristics (ROC) curve (A UC) by achieving at a lower computational runtime (3.89secs) Accuracy of94.5% and AUC of0.944 with the group-based Quran dataset.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Oyekunle, Adeleke Abdullahi
author_facet	Oyekunle, Adeleke Abdullahi
author_sort	Oyekunle, Adeleke Abdullahi
title	An enhanced feature selection technique for classification of group-based holy quran verses
title_short	An enhanced feature selection technique for classification of group-based holy quran verses
title_full	An enhanced feature selection technique for classification of group-based holy quran verses
title_fullStr	An enhanced feature selection technique for classification of group-based holy quran verses
title_full_unstemmed	An enhanced feature selection technique for classification of group-based holy quran verses
title_sort	enhanced feature selection technique for classification of group-based holy quran verses
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2018
url	http://eprints.uthm.edu.my/7549/2/24p%20ADELEKE%20ABDULLAHI%20OYEKUNLE.pdf http://eprints.uthm.edu.my/7549/1/ADELEKE%20ABDULLAHI%20OYEKUNLE%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/7549/3/ADELEKE%20ABDULLAHI%20OYEKUNLE%20WATERMARK.pdf
_version_	1747831166597070848

An enhanced feature selection technique for classification of group-based holy quran verses

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