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Improvising BayesNet Classifier Using Various Feature Reduction Method for Spam Classification

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Executive Summary

In this paper, the authors have proposed an efficient approach for the reduction of significant attributes from the Spam base warehouses for identifying spam email using forward selection method and have performed the classification of spam e-mail using data mining techniques. The data used in this paper is collected from UCI Machine Learning Repository Spam base dataset. The dataset consist of 4601 records which have 58 attributes and after applying Correlation - based Feature Selection methods the original attributes was reduced to 22, 16 and 8 potential attributes. They have investigated four data mining techniques such as J48, BayesNet, OneR and Classification via Clustering.

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