A comparison of data preparation approaches for e-mail categorisation. (21st August 2007)

• Record Type: Journal Article
• Authors: Berger, Helmut; Merkl, Dieter; Dittenbach, Michael
• Is Part Of: International journal of intelligent information and database systems. Volume 1:Number 2(2007)
• Page Start: 91
• Page End: 121
• Publication Date: 2007-08-21
• Subjects: e-mail categorisation -- document representation -- machine learning -- indexing methods -- information filtering -- feature selection -- data preparation -- support vector machines -- decision tree learners -- instance-based classifiers -- Bayes classification -- self-organising maps -- header information; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

This paper reports on experiments in multi-class e-mail categorisation with supervised and unsupervised machine learning techniques. To this end, Support Vector Machines, decision tree learners, instance-based classifiers, Naive Bayes classification approaches and Self-Organising Maps were applied. A word-based and a character n-gram document representation approach were employed in order to assess the categorisation performance of the various learning approaches. The results indicate a substantial increase in classification accuracy when e-mail header information is considered in the document representation. To a much lesser degree, word-based document representations are advantageous over n-gram representations.