Classification of textual E-mail spam using data mining techniques
Applied Computational Intelligence and Soft Computing
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In the last decade spam detection has been addressed as a text classification or categorization problem. In this paper we propose a new dynamic weighted voting method based on the combination of clustering and weighted voting, and apply it to the task of spam filtering. In order to classify a new sample, it first compares with all cluster centroids and its similarity to each cluster is identified; Classifiers in the vicinity of the input sample obtain greater weight for the final decision of the ensemble. The evaluation shows that the algorithm outperforms pure SVM.