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Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/7160

Title: E-mail classifications using Support Vector machine
Authors: Ali, Selamat
Mariah, Mohd. Daud
Keywords: Email classification;Support vector machine (SVM);Feature selection;Text categorization;Machine learning;Text processing (Computer science);Electronic mail systems
Issue Date: 14-May-2005
Publisher: Kolej Universiti Kejuruteraan Utara Malaysia
Citation: p.73-76
Series/Report no.: Proceedings of the 1st International Workshop on Artificial Life and Robotics
Abstract: - Study on text categorization field contains classification process of text documents into a fixed number of pre-defined categories by user. The paper studies on the classification of email messages based on their categories using Support Vector Machine (SVM). Among classification processes involved are reading input email data from its subject and body, feature extraction process, feature selection process, and feature classification process. Feature extraction process involves word stopping and stemming methods that can reduce the number of dimension of features. We have used the term frequency inverse document frequency (tfidf) for the features selection method. The effectiveness of classification results has been measured using precision and recall criteria. From experiments, the results have shown a higher accuracy in classifying email dataset using the SVM.
Description: Orgnized by Kolej Universiti Kejuruteraan Utara Malaysia (KUKUM), 14th - 15th May 2005 at Putra Palace Hotel, Kangar, Perlis.
URI: http://hdl.handle.net/123456789/7160
Appears in Collections:Conference Papers

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