Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Tree-Structured Support Vector Machines for Multi-class Pattern Recognition
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
A Framework for Adaptive Mail Classification
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
The Journal of Machine Learning Research
FASiL adaptive email categorization system
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
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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.