Using LPP and LS-SVM for spam filtering

To efficiently deal with spam mail filtering problem, a novel spam filtering algorithm based on locality pursuit projection (LPP) and least square version of SVM(LS-SVM) is proposed in this paper. The mail message features are first extracted by the LPP algorithm, then the LS-SVM classifier is used to classify mails into spam and legitimate. Experimental results demonstrate that the proposed algorithm performs much better than other related spam filtering algorithms.

[1]  Tianshun Yao,et al.  An evaluation of statistical spam filtering techniques , 2004, TALIP.

[2]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[3]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .

[4]  Jiawei Han,et al.  Document clustering using locality preserving indexing , 2005, IEEE Transactions on Knowledge and Data Engineering.

[5]  Alois Potton.  Spam , 2003, PIK Prax. Informationsverarbeitung Kommun..

[6]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[7]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[8]  Georgios Paliouras,et al.  A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists , 2004, Information Retrieval.

[9]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[10]  Georgios Paliouras,et al.  An evaluation of Naive Bayesian anti-spam filtering , 2000, ArXiv.