A New Probabilistic Model for Bayes Document Classification

DOI

10.2991/iccsee.2013.311How to use a DOI?

Keywords

naïve Bayes, text classification, Multi-variate Bernoulli event Model, Multinomial event Model

Abstract

In this paper, we propose a new probabilistic model of naïve Bayes method which can be used in text classification. This method not only takes account of the frequency of feature words, but also considers those important words do not appear in the test document, which overcomes the shortcoming of the Multi-variate Bernoulli event Model(MBM) and Multinomial event Model(MM). Experiments show that the method proposed in this paper has better classification result than those traditional methods.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ya-Shu Liu
AU  - Han-Bing Yan
PY  - 2013/03
DA  - 2013/03
TI  - A New Probabilistic Model for Bayes Document Classification
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 1239
EP  - 1242
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.311
DO  - 10.2991/iccsee.2013.311
ID  - Liu2013/03
ER  -