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2013 | 23 | 4 | 787-795
Tytuł artykułu

Learning the naive Bayes classifier with optimization models

Treść / Zawartość
Warianty tytułu
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.
Rocznik
Tom
23
Numer
4
Strony
787-795
Opis fizyczny
Daty
wydano
2013
otrzymano
2012-08-27
poprawiono
2013-03-12
poprawiono
2013-07-15
Twórcy
autor
  • Centre for Informatics and Applied Optimization, School of Science, Information Technology and Engineering, University of Ballarat, Victoria 3353, Australia
  • 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
Bibliografia
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv23z4p787bwm
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