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Novel Email Spam Classification using Integrated Particle Swarm Optimization and J48
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 149 - Number 7 |
| Year of Publication: 2016 |
| Authors: Harpreet Kaur, Ajay Sharma |
10.5120/ijca2016911466
|
Harpreet Kaur, Ajay Sharma . Novel Email Spam Classification using Integrated Particle Swarm Optimization and J48. International Journal of Computer Applications. 149, 7 ( Sep 2016), 23-27. DOI=10.5120/ijca2016911466
Abstract
E-mails have become an integral part of both private and professional lives and can also be studied as formal papers in communication between users. Several activities such as spam detection and classification, subject classification, etc. can be done by email’s data mining and analysis. Review has shown that the use of unsupervised filtering to filter the input data set is ignored by the most of the existing researchers. The use of hybridization of data mining techniques is ignored in order to improve the accuracy rate further for detection of fraudulent emails. Most of the existing techniques are limited to some significant features of emails therefore utilising more features may provide more significant results. The overall objective of this work is to propose an integrated particle swarm optimization based J48 algorithm to enhance the accuracy rate further.
References
- Tan, Pang-Ning, Michael Steinbach, and Vipin Kumar, “Introduction to data mining,” Vol. 1. Boston: Pearson Addison Wesley, 2006.
- Bharati, Mrs, and M. Ramageri "Data mining techniques and applications," (2010).
- Bai, Qinghai, "Analysis of particle swarm optimization algorithm," Computer and information science 3.1 (2010): 180.
- Khan, Amreen, N. G. Bawane, and Sonali Bodkhe, "An analysis of particle swarm optimization with data clustering-technique for optimization in data mining," (IJCSE) International Journal on Computer Science and Engineering 2.07 (2010): 2223-2226.
- Sarkar, Sunita, Arindam Roy, and Bipul Shyam Purkayastha,"Application of Particle Swarm Optimization in Data Clustering: A Survey," International Journal of Computer Applications 65.25, (2013).
- Shahreza, M. Lotfi, et al., "Anomaly detection using a self-organizing map and particle swarm optimization," Scientia Iranica 18.6 (2011): 1460-1468.
- Rini, Dian Palupi, Siti Mariyam Shamsuddin, and Siti Sophiyati Yuhaniz. "Particle swarm optimization: technique, system and challenges."International Journal of Computer Applications 14.1 (2011): 19-26.
- Al-Kadhi, Mishaal Abdullah, "Assessment of the status of spam in the Kingdom of Saudi Arabia," Journal of King Saud University-Computer and Information Sciences 23.2 (2011): 45-58.
- Kumar, R. Kishore, G. Poonkuzhali, and P. Sudhakar,"Comparative study on email spam classifier using data mining techniques," Proceedings of the International MultiConference of Engineers and Computer Scientists,Vol. 1, 2012.
- Jindal, Nitin, and Bing Liu,"Review spam detection," Proceedings of the 16th international conference on World Wide Web. ACM, 2007.
- Günal, Serkan, et al., "On feature extraction for spam e-mail detection,"Multimedia content representation, classification and security, Springer Berlin Heidelberg, 2006, 635-642.
- Alsmadi, Izzat, and Ikdam Alhami,"Clustering and classification of email contents," Journal of King Saud University-Computer and Information Sciences 27.1 (2015): 46-57.
- Rathi, Megha, and Vikas Pareek, "Spam Mail Detection through Data Mining-A Comparative Performance Analysis," International Journal of Modern Education and Computer Science 5.12 (2013): 31.
- Kumar, R. Kishore, G. Poonkuzhali, and P. Sudhakar, "Comparative study on email spam classifier using data mining techniques," Proceedings of the International MultiConference of Engineers and Computer Scientists, Vol. 1, 2012.
- Elssied, Nadir Omer Fadl, Othman Ibrahim, and Waheeb Abu-Ulbeh,"AN IMPROVED OF SPAM E-MAIL CLASSIFICATION MECHANISM USING K-MEANS CLUSTERING," Journal of Theoretical & Applied Information Technology 60.3 (2014).
- Pérez-Díaz, Noemí, et al.,"Rough sets for spam filtering: Selecting appropriate decision rules for boundary e-mail classification," Applied Soft Computing 12.11 (2012): 3671-3682.
- Salehi, Saber, and Ali Selamat,"Hybrid simple artificial immune system (SAIS) and particle swarm optimization (PSO) for spam detection," Software Engineering (MySEC), 2011 5th Malaysian Conference in. IEEE, 2011.
- Sharma, Amit Kumar, and Renuka Yadav, "Spam Mails Filtering Using Different Classifiers with Feature Selection and Reduction Technique,"Communication Systems and Network Technologies (CSNT), 2015 Fifth International Conference on. IEEE, 2015.
- Vyas, Tarjani, Payal Prajapati, and Somil Gadhwal, "A survey and evaluation of supervised machine learning techniques for spam e-mail filtering,"Electrical, Computer and Communication Technologies (ICECCT), 2015 IEEE International Conference on. IEEE, 2015.
- Qian, Feng, et al., "A case for unsupervised-learning-based spam filtering,"ACM SIGMETRICS Performance Evaluation Review, Vol. 38, No. 1. ACM, 2010.
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