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Soft Computing

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01.01.2010 | Focus

A three-layer back-propagation neural network for spam detection using artificial immune concentration

verfasst von: Guangchen Ruan, Ying Tan

Erschienen in: Soft Computing | Ausgabe 2/2010

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Abstract

In this paper, a three-layer back-propagation neural network (BPNN) is employed for spam detection by using a concentration based feature construction (CFC) approach. In the CFC approach, ‘self’ and ‘non-self’ concentrations are constructed through ‘self’ and ‘non-self’ gene libraries, respectively, to form a two-element concentration vector for expressing the e-mail efficiently. A three-layer BPNN with two-element input is then employed to classify e-mails automatically. Comprehensive experiments are conducted on two public benchmark corpora PU1 and Ling to demonstrate that the proposed CFC approach based BPNN classifier not only has a very much fast speed but also achieves 97 and 99% of classification accuracy on corpora PU1 and Ling by just using a two-element concentration feature vector.

Vorheriger Artikel Content-based image classification with wavelet relevance vector machines

Nächster Artikel k-Top Scoring Pair Algorithm for feature selection in SVM with applications to microarray data classification

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The PU1 corpus and Ling corpus may be downloaded from http://www.cil.pku.edu.cn/resources/.

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Titel: A three-layer back-propagation neural network for spam detection using artificial immune concentration
verfasst von: Guangchen Ruan
Ying Tan
Publikationsdatum: 01.01.2010
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 2/2010
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-009-0440-2

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