Hybrid simple artificail immune system (SAIS) ans particle swarm optimization (PSO) for spam detection

Spam detection is a significant problem which considered by many researchers by various developed strategies. Among many others, simple artificial immune system is one of those being proposed. There is a deficiency in number of optimization methods in simple artificial immune system (SAIS). This pro...

Full description

Saved in:
Bibliographic Details
Main Authors: Salehi, Saber, Selamat, Ali
Format: Conference or Workshop Item
Published: 2011
Subjects:
Online Access:http://eprints.utm.my/id/eprint/45925/
http://ieeexplore.ieee.org.ezproxy.utm.my/document/6140655/
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Teknologi Malaysia
id my.utm.45925
record_format eprints
spelling my.utm.459252017-08-29T01:17:42Z http://eprints.utm.my/id/eprint/45925/ Hybrid simple artificail immune system (SAIS) ans particle swarm optimization (PSO) for spam detection Salehi, Saber Selamat, Ali T Technology (General) Spam detection is a significant problem which considered by many researchers by various developed strategies. Among many others, simple artificial immune system is one of those being proposed. There is a deficiency in number of optimization methods in simple artificial immune system (SAIS). This problem can be solved and eliminated using other optimization methods besides mutation. 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. Achieved results represent the Hybrid SAIS and PSO is superior to that of a SAIS. 2011 Conference or Workshop Item PeerReviewed Salehi, Saber and Selamat, Ali (2011) Hybrid simple artificail immune system (SAIS) ans particle swarm optimization (PSO) for spam detection. In: 2011 5th Malaysian Software Engineering Conference (MYSEC). http://ieeexplore.ieee.org.ezproxy.utm.my/document/6140655/
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic T Technology (General)
spellingShingle T Technology (General)
Salehi, Saber
Selamat, Ali
Hybrid simple artificail immune system (SAIS) ans particle swarm optimization (PSO) for spam detection
description Spam detection is a significant problem which considered by many researchers by various developed strategies. Among many others, simple artificial immune system is one of those being proposed. There is a deficiency in number of optimization methods in simple artificial immune system (SAIS). This problem can be solved and eliminated using other optimization methods besides mutation. 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. Achieved results represent the Hybrid SAIS and PSO is superior to that of a SAIS.
format Conference or Workshop Item
author Salehi, Saber
Selamat, Ali
author_facet Salehi, Saber
Selamat, Ali
author_sort Salehi, Saber
title Hybrid simple artificail immune system (SAIS) ans particle swarm optimization (PSO) for spam detection
title_short Hybrid simple artificail immune system (SAIS) ans particle swarm optimization (PSO) for spam detection
title_full Hybrid simple artificail immune system (SAIS) ans particle swarm optimization (PSO) for spam detection
title_fullStr Hybrid simple artificail immune system (SAIS) ans particle swarm optimization (PSO) for spam detection
title_full_unstemmed Hybrid simple artificail immune system (SAIS) ans particle swarm optimization (PSO) for spam detection
title_sort hybrid simple artificail immune system (sais) ans particle swarm optimization (pso) for spam detection
publishDate 2011
url http://eprints.utm.my/id/eprint/45925/
http://ieeexplore.ieee.org.ezproxy.utm.my/document/6140655/
_version_ 1643651883821170688