Instance Subset Selection in SMS Classification using PSO-SVM

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R. Parimala
Dr. R. Nallaswamy

Abstract

Text categorization is the task of classifying natural language documents into a set of predefined categories. It can provide conceptual views of document collections and has important applications in the real world. Short messages often consist of only a few words, and therefore present a traditional bag-of-words based spam filters using R package. In this paper we analyze the concept of a new classification model which will classify Mobile SMS into predefined classes. We have tested feasibility of applying Support Vector Machine (SVM) based machine learning techniques reported most effective in SMS spam filtering on NUS SMS dataset. We see that bag of-words filters improved substantially using different features. We conclude that classification for short messages is surprisingly effective.

Keywords: Text classification, pre-processing, sparse term, Support Vector Machine, Particle swarm optimization

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