Spam filtering email classification (SFECM) using gain and graph mining algorithm

M. K. Chae, Abeer Alsadoon, P. W.C. Prasad, Sasikumaran Sreedharan

Research output: Book chapter/Published conference paperConference paperpeer-review

3 Citations (Scopus)


This paper proposes a hybrid solution of spam email classifier using context based email classification model as main algorithm complimented by information gain calculation to increase spam classification accuracy. Proposed solution consists of three stages email pre-processing, feature extraction and email classification. Research has found that LingerIG spam filter is highly effective at separating spam emails from cluster of homogenous work emails. Also experiment result proved the accuracy of spam filtering is 100% as recorded by the team of developers at University of Sydney. The study has shown that implementing the spam filter in the context-based email classification model is feasible. Experiment of the study has confirmed that spam filtering aspect of context-based classification model can be improved.
Original languageEnglish
Title of host publication2017 2nd International Conference on Anti-Cyber Crimes (ICACC)
EditorsSalem Alelyani , Justin Varghese , Nasser Tairan , Fahad Alahmari , Ali Aseere , Amjad Mohana , Adnan Al Bar
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781509058143
ISBN (Print)9781509058150 (Print on demand)
Publication statusPublished - 24 Apr 2017
Event2nd International Conference on Anti-Cyber Crimes: ICACC 2017 - King Khalid University, Abha, Saudi Arabia
Duration: 26 Mar 201727 Mar 2017 (Conference proceedings)


Conference2nd International Conference on Anti-Cyber Crimes
Country/TerritorySaudi Arabia
Internet address


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