A comparative evaluation of machine learning approaches in SMS spam detection

Spam detection is a significant problem which is considered by many researchers by various developed strategies. In this study, the popular performance measure is a classification accuracy which deals with false positive, false negative and accuracy. These metrics were evaluated under applying three...

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Main Author:	Salehi, Saber
Format:	Thesis
Language:	English
Published:	2011
Subjects:	HD Industries Land use Labor
Online Access:	http://eprints.utm.my/id/eprint/32801/5/SaberSalehiMFSKSM2011.pdf
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id	my-utm-ep.32801
record_format	uketd_dc
spelling	my-utm-ep.328012018-05-27T07:54:55Z A comparative evaluation of machine learning approaches in SMS spam detection 2011-07 Salehi, Saber HD Industries. Land use. Labor Spam detection is a significant problem which is considered by many researchers by various developed strategies. In this study, the popular performance measure is a classification accuracy which deals with false positive, false negative and accuracy. These metrics were evaluated under applying three supervised learning algorithm (Hybrid of Simple Artificial Immune System (SAIS) and Particle Swarm Optimization (PSO), Naive Bayes Classifier (NBC), Enhanced Genetic Algorithm (EGA)) based on classification of SMS contents were evaluated and compared. In this research, SAIS was hybridized by particle swarm optimization (PSO) for optimizing the performance of SAIS for spam filtering. PSO was used with mutation to reinforce the immune system’s searches to find the best class in exemplar for classification. Results were improved using Hybrid SAIS and PSO. The proposed EGA was to achieve the best chromosomes which were grouped by the keywords. Then, the best chromosome with highest fitness value was selected as classifier. Simulated annealing (SA) was used with classical mutation and crossover to reinforce the efficiency of genetic searches. Achieved results represent the enhanced GA is markedly superior to that of a classical GA. These algorithms were trained and tested on a set of 4601 SMS messages in which 1813 were spams and 2788 were non-spams. Results showed that the proposed EGA technique gave better result compare to the hybrid SAIS and PSO and NBC techniques. Results also showed that the proposed EGA technique gave 99.87% accuracy, and the proposed NBC, hybrid of SAIS and PSO techniques gave 97.457% and 88.33% accuracy, respectively. 2011-07 Thesis http://eprints.utm.my/id/eprint/32801/ http://eprints.utm.my/id/eprint/32801/5/SaberSalehiMFSKSM2011.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	HD Industries Land use Labor
spellingShingle	HD Industries Land use Labor Salehi, Saber A comparative evaluation of machine learning approaches in SMS spam detection
description	Spam detection is a significant problem which is considered by many researchers by various developed strategies. In this study, the popular performance measure is a classification accuracy which deals with false positive, false negative and accuracy. These metrics were evaluated under applying three supervised learning algorithm (Hybrid of Simple Artificial Immune System (SAIS) and Particle Swarm Optimization (PSO), Naive Bayes Classifier (NBC), Enhanced Genetic Algorithm (EGA)) based on classification of SMS contents were evaluated and compared. In this research, SAIS was hybridized by particle swarm optimization (PSO) for optimizing the performance of SAIS for spam filtering. PSO was used with mutation to reinforce the immune system’s searches to find the best class in exemplar for classification. Results were improved using Hybrid SAIS and PSO. The proposed EGA was to achieve the best chromosomes which were grouped by the keywords. Then, the best chromosome with highest fitness value was selected as classifier. Simulated annealing (SA) was used with classical mutation and crossover to reinforce the efficiency of genetic searches. Achieved results represent the enhanced GA is markedly superior to that of a classical GA. These algorithms were trained and tested on a set of 4601 SMS messages in which 1813 were spams and 2788 were non-spams. Results showed that the proposed EGA technique gave better result compare to the hybrid SAIS and PSO and NBC techniques. Results also showed that the proposed EGA technique gave 99.87% accuracy, and the proposed NBC, hybrid of SAIS and PSO techniques gave 97.457% and 88.33% accuracy, respectively.
format	Thesis
qualification_level	Master's degree
author	Salehi, Saber
author_facet	Salehi, Saber
author_sort	Salehi, Saber
title	A comparative evaluation of machine learning approaches in SMS spam detection
title_short	A comparative evaluation of machine learning approaches in SMS spam detection
title_full	A comparative evaluation of machine learning approaches in SMS spam detection
title_fullStr	A comparative evaluation of machine learning approaches in SMS spam detection
title_full_unstemmed	A comparative evaluation of machine learning approaches in SMS spam detection
title_sort	comparative evaluation of machine learning approaches in sms spam detection
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2011
url	http://eprints.utm.my/id/eprint/32801/5/SaberSalehiMFSKSM2011.pdf
_version_	1747816063771344896

A comparative evaluation of machine learning approaches in SMS spam detection

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