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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Binarization Approaches to Email Categorization
verfasst von : Yunqing Xia, Kam-Fai Wong
Erschienen in: Computer Processing of Oriental Languages. Beyond the Orient: The Research Challenges Ahead
Verlag: Springer Berlin Heidelberg
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Email categorization becomes very popular today in personal information management. However, most n-way classification methods suffer from feature unevenness problem, namely, features learned from training samples distribute unevenly in various folders. We argue that the binarization approaches can handle this problem effectively. In this paper, three binarization techniques are implemented, i.e. one-against-rest, one-against-one and some-against-rest, using two assembling techniques, i.e. round robin and elimination. Experiments on email categorization prove that significant improvement has been achieved in these binarization approaches over an n-way baseline classifier.