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Automatic document classification based on probabilistic reasoning: model and performance analysis

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2 Author(s)
Wai Lam ; Dept. of Syst. Eng. & Eng. Manage., Chinese Univ. of Hong Kong, Shatin, Hong Kong ; Kon-Fan Low

We develop a new approach to test classification based on automatic feature extraction and probabilistic reasoning. The knowledge representation used to perform such task is known as Bayesian inference networks. A Bayesian network text classifier is automatically constructed from a set of training test documents. We have conducted a series of experiments on two text document corpus, namely the CACM and Reuters, to analyze the performance of our approach, which are described in the paper

Published in:

Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on  (Volume:3 )

Date of Conference:

12-15 Oct 1997