Learning the naive Bayes classifier with optimization models

• Autorzy: Sona Taheri , Musa Mammadov
• Języki publikacji: EN

Abstrakty

EN

Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are calculated using training data, and then values of these probabilities are used to classify new observations. In this paper, we introduce three novel optimization models for the naive Bayes classifier where both class probabilities and conditional probabilities are considered as variables. The values of these variables are found by solving the corresponding optimization problems. Numerical experiments are conducted on several real world binary classification data sets, where continuous features are discretized by applying three different methods. The performances of these models are compared with the naive Bayes classifier, tree augmented naive Bayes, the SVM, C4.5 and the nearest neighbor classifier. The obtained results demonstrate that the proposed models can significantly improve the performance of the naive Bayes classifier, yet at the same time maintain its simple structure.

Słowa kluczowe

EN

Bayesian networks; naive Bayes classifier; optimization; discretization

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2013
• Tom: 23
• Numer: 4
• Strony: 787-795

Daty

wydano

2013

otrzymano

2012-08-27

poprawiono

2013-03-12

poprawiono

2013-07-15

Twórcy

autor

Sona Taheri

• Centre for Informatics and Applied Optimization, School of Science, Information Technology and Engineering, University of Ballarat, Victoria 3353, Australia

autor

Musa Mammadov

• Centre for Informatics and Applied Optimization, School of Science, Information Technology and Engineering, University of Ballarat, Victoria 3353, Australia
• Victoria Research Laboratory, National ICT Australia, Victoria 3010, Australia

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Identyfikatory

DOI

10.2478/amcs-2013-0059