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Improving Naive Bayes classifier using conditional probabilities

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conference contribution
posted on 2024-06-05, 03:23 authored by S Taheri, Musa MammadovMusa Mammadov, AM Bagirov
Naive Bayes classifier is the simplest among Bayesian Network classifiers. It has shown to be very efficient on a variety of data classification problems. However, the strong assumption that all features are conditionally independent given the class is often violated on many real world applications. Therefore, improvement of the Naive Bayes classifier by alleviating the feature independence assumption has attracted much attention. In this paper, we develop a new version of the Naive Bayes classifier without assuming independence of features. The proposed algorithm approximates the interactions between features by using conditional probabilities. We present results of numerical experiments on several real world data sets, where continuous features are discretized by applying two different methods. These results demonstrate that the proposed algorithm significantly improve the performance of the Naive Bayes classifier, yet at the same time maintains its robustness. © 2011, Australian Computer Society, Inc.

History

Volume

121

Pagination

63-68

Location

Ballarat, Vic.

Open access

  • Yes

Start date

2011-12-01

End date

2011-12-02

ISSN

1445-1336

ISBN-13

9781921770029

Language

eng

Publication classification

EN.1 Other conference paper

Copyright notice

2011, Australian Computer Society

Title of proceedings

AusDM11 : Proceedings of the 9th Australasian Data Mining Conference.

Event

Australasian Data Mining. Conference (9th : 2011 : Ballarat, Victoria)

Publisher

Australian Computer Society

Place of publication

Sydney, N.S.W.

Series

Conferences in Research and Practice in Information Technology Series

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