AIRCC's International Journal of Computer Science and Information Technology

W. A. Awad ¹

Affiliations
1 Math and Computer Science Dept., Port Said University, EG

Abstract

In this paper we use machine learning algorithms like SVM, KNN and GIS to perform a behavior comparison on the web pages classifications problem, from the experiment we see in the SVM with small number of negative documents to build the centroids has the smallest storage requirement and the least on line test computation cost. But almost all GIS with different number of nearest neighbors have an even higher storage requirement and on line test computation cost than KNN. This suggests that some future work should be done to try to reduce the storage requirement and on list test cost of GIS.

Keywords

Web Classifications, Machine Learning, LIBSVM, SVM, K-NN.

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W. A. Awad ¹, S. M. ELseuofi ²

Affiliations
1 Math. and Comp. Sci. Dept., Port Said University, EG
2 Inf. System Dept., Ras El Bar High Inst., EG

Abstract

The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable anti-spam filters. Machine learning techniques now days used to automatically filter the spam e-mail in a very successful rate. In this paper we review some of the most popular machine learning methods (Bayesian classification, k-NN, ANNs, SVMs, Artificial immune system and Rough sets) and of their applicability to the problem of spam Email classification. Descriptions of the algorithms are presented, and the comparison of their performance on the SpamAssassin spam corpus is presented.

Keywords

Spam, E-Mail Classification, Machine Learning Algorithms.

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