Name
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1504/IJIIDS.2007.014946 | IJIIDS |
Keywords | Field | DocType |
character n-gram document representation,data preparation approach,document representation,various learning approach,word-based document representation,e-mail header information,classification accuracy,n-gram representation,multi-class e-mail categorisation,categorisation performance,naive bayes classification approach,feature selection,machine learning,support vector machines | Data mining,Decision tree,Feature selection,Naive Bayes classifier,Computer science,Support vector machine,Unsupervised learning,Artificial intelligence,Header,Linear classifier,Data preparation,Machine learning | Journal |
Volume | Issue | Citations |
1 | 2 | 0 |
PageRank | References | Authors |
0.34 | 25 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Helmut Berger | 1 | 0 | 0.34 |
| Dieter Merkl | 2 | 846 | 115.65 |
| Michael Dittenbach | 3 | 297 | 26.48 |