By Topic

Concentration based feature construction approach for spam detection

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Ying Tan ; Dept. of Machine Intell., Peking Univ., Beijing, China ; Chao Deng ; Guangchen Ruan

Inspired by human immune system, a concentration based feature construction (CFC) approach which utilizes a two-element concentration vector as the feature vector is proposed for spam detection in this paper. In the CFC approach, dasiaselfpsila and dasianon-selfpsila concentrations are constructed by using dasiaselfpsila and dasianon-selfpsila gene libraries, respectively, and subsequently are used to form a vector with two elements of concentrations for characterizing the e-mail efficiently. As a result, the design of classifier actually amounts to establishing a mapping between two real-value inputs and one binary output. The classification of the e-mail is considered as an optimization problem aiming at minimizing a formulated cost function. A clonal particle swarm optimization (CPSO) algorithm proposed by the leading author is also employed for this purpose. Several classifiers including linear discriminant, multi-layer neural networks and support vector machine are used to verify the effectiveness and robustness of the CFC approach. Experimental results demonstrate that the proposed CFC approach not only has a very much fast speed but also gives 97% and 99% of accuracy just using a two-element concentration feature vector on corpus PU1 and Ling, respectively.

Published in:

Neural Networks, 2009. IJCNN 2009. International Joint Conference on

Date of Conference:

14-19 June 2009