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

Author

Lam, Wai ; Low, Kon-Fan

Author_Institution

Dept. of Syst. Eng. & Eng. Manage., Chinese Univ. of Hong Kong, Shatin, Hong Kong

Volume

3

fYear

1997

fDate

12-15 Oct 1997

Firstpage

2719

Abstract

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

Keywords

Bayes methods; document handling; feature extraction; inference mechanisms; knowledge representation; pattern classification; performance evaluation; probability; Bayesian inference networks; automatic document classification; feature extraction; knowledge representation; performance evaluation; probabilistic reasoning; text classifier; Bayesian methods; Feature extraction; Information retrieval; Knowledge representation; Performance analysis; Research and development management; Routing; Systems engineering and theory; Text categorization; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on

Conference_Location

Orlando, FL

ISSN

1062-922X

Print_ISBN

0-7803-4053-1

Type

conf

DOI

10.1109/ICSMC.1997.635349

Filename

635349