This paper purports a refreshing spam discovery technology for chat system based on rough set theory. Nowadays, spam is very much allied with a huge chunk of data transferred through internet involving all disturbing and unsolicited contents received via different web-services such as chat systems, e-mail, forums and web logs. In this paper, we have reviewed various past research works of filtering SPAM and propose a novel filtering technique for SPAM especially for chat system with the support of classical rough set theory. Simulation results clearly indicate that our proposed method, can achieve higher accuracy in spam detection as compared to the existing strategies. © 2013 IEEE.

Sanjiban Sekhar Roy

Department of Software Systems

School of Computer Science and Engineering

Vellore Campus

Charaborty S

Sourav S

Abraham A.

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Rough set theory approach for filtering spams from boundary messages in a chat system

2013 13th International Conference on Intellient Systems Design and Applications

Spam emails have become an increasing difficulty for the entire web-users.These unsolicited messages waste the resources of network unnecessarily. Customarily, machine learning techniques are adopted for filtering email spam. This article examines the capabilities of the extreme learning machine (ELM) and support vector machine (SVM) for the classification of spam emails with the class level (d). The ELM method is an efficient model based on single layer feed-forward neural network, which can choose weights from hidden layers,randomly. Support vector machine is a strong statistical learning theory used frequently for classification. The performance of ELM has been compared with SVM. The comparative study examines accuracy, precision, recall, false positive, true positive.Moreover, a sensitivity analysis has been performed by ELM and SVM for spam email classification.

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Applied Soft Computing

Rough sets for spam filtering: Selecting appropriate decision rules for boundary e-mail classification

Journal	Data powered by Typeset2013 13th International Conference on Intellient Systems Design and Applications
Publisher	Data powered by TypesetIEEE
ISSN	21647143
Open Access	Yes