Call for Paper - August 2022 Edition
IJCA solicits original research papers for the August 2022 Edition. Last date of manuscript submission is July 20, 2022. Read More

Improving E-Mail Spam Classification using Ant Colony Optimization Algorithm

Print
PDF
IJCA Proceedings on International Conference on Innovations in Computing Techniques (ICICT 2015)
© 2015 by IJCA Journal
ICICT 2015 - Number 2
Year of Publication: 2015
Authors:
D. Karthika Renuka
P. Visalakshi
T. Sankar

D.karthika Renuka, P.visalakshi and T.sankar. Article: Improving E-Mail Spam Classification using Ant Colony Optimization Algorithm. IJCA Proceedings on International Conference on Innovations in Computing Techniques (ICICT 2015) ICICT 2015(2):22-26, July 2015. Full text available. BibTeX

@article{key:article,
	author = {D.karthika Renuka and P.visalakshi and T.sankar},
	title = {Article: Improving E-Mail Spam Classification using Ant Colony Optimization Algorithm},
	journal = {IJCA Proceedings on International Conference on Innovations in Computing Techniques (ICICT 2015)},
	year = {2015},
	volume = {ICICT 2015},
	number = {2},
	pages = {22-26},
	month = {July},
	note = {Full text available}
}

Abstract

In recent days, Electronic mail system is a store and forward mechanism used for the purpose of exchanging documents across computer network through Internet. Spam is an unwanted mail which contains unsolicited and harmful data that are irrelevant to the specified users. In the proposed system, the spam classification is implemented using Naive Bayes classifier, which is a probabilistic classifier based on conditional probability applicable for more complex classification problems. Implementation of feature selection using hybrid Ant Colony Optimization serves to be more efficient which gives good results for the above system that has been proposed in this paper.

References

  • Mehdi Hosseinzadeh Aghdam, Nasser Ghasem-Aghaee, Mohammad Ehsan Basiri, "Text feature selection using ant colony optimization", Expert System with application, Vol. 36 No. 6843-6853, 2009.
  • W. A. Awad and S. M. ELseuofi, "Machine learning methods for Spam E-mail Classification", International Journal of Computer Science & Information Technology (IJCSIT), Vol 3, No 1, February 2011.
  • Bolun Chen, LingChen and YixinChen, "Efficient ant colony optimization for image feature selection", Signal Processing Vol. 93, No. 1566–1576, 2013.
  • Shahla Nemati, Mohammad Ehsan Basiri, Nasser Ghasem-Aghaee, Mehdi Hosseinzadeh Aghdam," A novel ACO–GA hybrid algorithm for feature selection in protein function prediction", Expert Systems with Applications Vol. 36, No. 12086–12094, 2009.
  • David Martens, Manu De Backer, Raf Haesen, "Classification With Ant Colony Optimization", IEEE transactions on Evolutionary Computation, vol. 11, No. 5, october 2007.
  • Rahul Karthik Sivagaminathan, Sreeram Ramakrishnan, "A hybrid approach for feature subset selection using neural networks and ant colony optimization", Expert Systems with Applications Vol. 33, No. 49–60, 2007.
  • C. -L. Huang, C. -J. Wang, "A GA-based feature selection and parameters optimization for support vector machines", Expert Systems with Applications, Vol. 31, No 231–240, 2006.
  • L. -Y. Chuang, H. -W. Chang, C. -J. Tu, C. -H. Yang, "Improved binary PSO for feature selection using gene expression data", Computational Biology and Chemistry Vol. 32(1), No. 29–38, 2008.
  • S. Tasci, T. Gungor, "An evaluation of existing and new feature selection metrics in text categorization", International conference on Computer and Information Sciences, October 2008.
  • Yumin Chen, Duoqian Miao, Ruizhi Wang, "A rough set approach to feature selection based on ant colony optimization", Pattern Recognition Letters, Vol. 31, No. 226–233, 2010.
  • A. A. Mousaa,b, Waiel F. Abd El-Wahedc, R. M. Rizk-Allaha, "A hybrid ant colony optimization approach based local search scheme for multi objective design optimizations", Electric Power Systems Research, Vol. 81, No. 1014–1023, 2011.
  • Chi-Yao Tseng, Pin-Chieh Sung, and Ming-Syan Chen, "A Collaborative Spam Detection System with a Novel E-Mail Abstraction Scheme", IEEE Transactions on Knowledge and Data Engineering, Vol. 23, No. 5, May 2011.