A Study on Analysis of SMS Classification Using Document Frequency Thresold
Автор: R.Parimala, R. Nallaswamy
Журнал: International Journal of Information Engineering and Electronic Business(IJIEEB) @ijieeb
Статья в выпуске: 1 vol.4, 2012 года.
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Recent years, feature selection is chief concern in text classification. A major characteristic in text classification is the high dimensionality of the feature space. Therefore, feature selection is strongly considered as one of the crucial part in text document categorization. Selecting the best features to represent documents can reduce the dimensionality of feature space hence increase the performance. Feature selection is performed here using Document Frequency Threshold. This paper focus on SVM based text message classification using document frequency threshold. The experiment is performed with NUS SMS text messages data set. An experimental result shows that the results of proposed method are more efficient.
Text Mining, Support Vector Machine, Document Term Matrix, Document frequency threshold
Короткий адрес: https://sciup.org/15013108
IDR: 15013108
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