Evolutionary symbiotic feature selection for email spam detection.
Presented at: UNSPECIFIED.
This work presents a symbiotic filtering approach enabling the exchange of relevant word features among different users in order to improve local anti-spam filters. The local spam filtering is based on a Content- Based Filtering strategy, where word frequencies are fed into a Naive Bayes learner. Several Evolutionary Algorithms are explored for feature selection, including the proposed symbiotic exchange of the most relevant features among different users. The experiments were conducted using a novel corpus based on the well known Enron datasets mixed with recent spam. The obtained results show that the symbiotic approach is competitive. Copyright © 2012 SciTePress.
|Type:||Conference item (UNSPECIFIED)|
|Title:||Evolutionary symbiotic feature selection for email spam detection|
|Keywords:||Collaborative filtering, Content-based filtering, Evolutionary algorithms, Feature selection, Naive bayes, Spam email, Symbiotic filtering, Text classification|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Electronic and Electrical Engineering
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