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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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  • Polanska, J., Borys, D. and Polanski, A. (2006). Node assignment problem in Bayesian networks, International Journal of Applied Mathematics and Computer Science 16(2): 233-240.
  • Taheri, S. and Mammadov, M. (2012). Structure learning of Bayesian networks using a new unrestricted dependency algorithm, IMMM 2012: The 2nd International Conference on Advances in Information on Mining and Management, Venice, Italy, pp. 54-59.
  • Taheri, S., Mammadov, M. and Bagirov, A. (2011). Improving naive Bayes classifier using conditional probabilities, 9th Australasian Data Mining Conference, Ballarat, Australia, pp. 63-68.
  • Taheri, S., Mammadov, M. and Seifollahi, S. (2012). Globally convergent algorithms for solving unconstrained optimization problems, Optimization: 1-15.
  • Tóth, L., Kocsor, A. and Csirik, J. (2005). On naive Bayes in speech recognition, International Journal of Applied Mathematics and Computer Science 15(2): 287-294.
  • Wu, X., Vipin Kumar, J., Quinlan, R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, J., Ng, A., Liu, B., Yu, P. S., Zhou, Z., Steinbach, M., Hand, D. J. and Steinberg, D. (2008). Top 10 algorithms in data mining, Knowledge and Information Systems 14: 1-37.
  • Yatsko, A., Bagirov, A.M. and Stranieri, A. (2011). On the discretization of continuous features for classification, Proceedings of the 9th Australasian Data Mining Conference (AusDM 2011), Ballarat, Australia, Vol. 125.
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Bibliografia

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