The performance of soft computing techniques on content-based SMS spam filtering

Content-based filtering is one of the most widely used methods to combat SMS (Short Message Service) spam. This method represents SMS text messages by a set of selected features which are extracted from data sets. Most of the available data sets have imbalanced class distribution problem. However...

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Main Author:	Hassan Saeed, Waddah Waheeb
Format:	Thesis
Language:	English English English
Published:	2015
Subjects:	QA76 Computer software
Online Access:	http://eprints.uthm.edu.my/1496/2/WADDAH%20WAHEEB%20HASSAN%20SAEED%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1496/1/24p%20WADDAH%20WAHEEB%20HASSAN%20SAEED.pdf http://eprints.uthm.edu.my/1496/3/WADDAH%20WAHEEB%20HASSAN%20SAEED%20WATERMARK.pdf
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spelling	my-uthm-ep.14962021-10-03T07:44:57Z The performance of soft computing techniques on content-based SMS spam filtering 2015-02 Hassan Saeed, Waddah Waheeb QA76 Computer software Content-based filtering is one of the most widely used methods to combat SMS (Short Message Service) spam. This method represents SMS text messages by a set of selected features which are extracted from data sets. Most of the available data sets have imbalanced class distribution problem. However, not much attention has been paid to handle this problem which affect the characteristics and size of selected features and cause undesired performance. Soft computing approaches have been applied successfully in content-based spam filtering. In order to enhance soft computing performance, suitable feature subset should be selected. Therefore, this research investigates how well suited three soft computing techniques: Fuzzy Similarity, Artificial Neural Network and Support Vector Machines (SVM) are for content-based SMS spam filtering using an appropriate size of features which are selected by the Gini Index metric as it has the ability to extract suitable features from imbalanced data sets. The data sets used in this research were taken from three sources: UCI repository, Dublin Institute of Technology (DIT) and British English SMS. The performance of each of the technique was compared in terms of True Positive Rate against False Positive Rate, F1 score and Matthews Correlation Coefficient. The results showed that SVM with 150 features outperformed the other techniques in all the comparison measures. The average time needed to classify an SMS text message is a fraction of a millisecond. Another test using NUS SMS corpus was conducted in order to validate the SVM classifier with 150 features. The results again proved the efficiency of the SVM classifier with 150 features for SMS spam filtering with an accuracy of about 99.2%. 2015-02 Thesis http://eprints.uthm.edu.my/1496/ http://eprints.uthm.edu.my/1496/2/WADDAH%20WAHEEB%20HASSAN%20SAEED%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/1496/1/24p%20WADDAH%20WAHEEB%20HASSAN%20SAEED.pdf text en public http://eprints.uthm.edu.my/1496/3/WADDAH%20WAHEEB%20HASSAN%20SAEED%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Hassan Saeed, Waddah Waheeb The performance of soft computing techniques on content-based SMS spam filtering
description	Content-based filtering is one of the most widely used methods to combat SMS (Short Message Service) spam. This method represents SMS text messages by a set of selected features which are extracted from data sets. Most of the available data sets have imbalanced class distribution problem. However, not much attention has been paid to handle this problem which affect the characteristics and size of selected features and cause undesired performance. Soft computing approaches have been applied successfully in content-based spam filtering. In order to enhance soft computing performance, suitable feature subset should be selected. Therefore, this research investigates how well suited three soft computing techniques: Fuzzy Similarity, Artificial Neural Network and Support Vector Machines (SVM) are for content-based SMS spam filtering using an appropriate size of features which are selected by the Gini Index metric as it has the ability to extract suitable features from imbalanced data sets. The data sets used in this research were taken from three sources: UCI repository, Dublin Institute of Technology (DIT) and British English SMS. The performance of each of the technique was compared in terms of True Positive Rate against False Positive Rate, F1 score and Matthews Correlation Coefficient. The results showed that SVM with 150 features outperformed the other techniques in all the comparison measures. The average time needed to classify an SMS text message is a fraction of a millisecond. Another test using NUS SMS corpus was conducted in order to validate the SVM classifier with 150 features. The results again proved the efficiency of the SVM classifier with 150 features for SMS spam filtering with an accuracy of about 99.2%.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Hassan Saeed, Waddah Waheeb
author_facet	Hassan Saeed, Waddah Waheeb
author_sort	Hassan Saeed, Waddah Waheeb
title	The performance of soft computing techniques on content-based SMS spam filtering
title_short	The performance of soft computing techniques on content-based SMS spam filtering
title_full	The performance of soft computing techniques on content-based SMS spam filtering
title_fullStr	The performance of soft computing techniques on content-based SMS spam filtering
title_full_unstemmed	The performance of soft computing techniques on content-based SMS spam filtering
title_sort	performance of soft computing techniques on content-based sms spam filtering
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Computer Science and Information Technology
publishDate	2015
url	http://eprints.uthm.edu.my/1496/2/WADDAH%20WAHEEB%20HASSAN%20SAEED%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1496/1/24p%20WADDAH%20WAHEEB%20HASSAN%20SAEED.pdf http://eprints.uthm.edu.my/1496/3/WADDAH%20WAHEEB%20HASSAN%20SAEED%20WATERMARK.pdf
_version_	1747830803005440000

The performance of soft computing techniques on content-based SMS spam filtering

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