Learning the naive Bayes classifier with optimization models

Asuncion, A. and Newman, D. (2007). UCI machine learning repository, http://www.ics.uci.edu/mlearn/mlrepository.Search in Google Scholar

Campos, M., Fernandez-Luna, Gamez, A. and Puerta, M. (2002). Ant colony optimization for learning Bayesian networks, International Journal of Approximate Reasoning 31(3): 291-311.10.1016/S0888-613X(02)00091-9Search in Google Scholar

Chang, C. and Lin, C. (2001). LIBSVM: A library for support vector machines, http://www.csie.ntu.edu.tw/cjlin/libsvm.Search in Google Scholar

Chickering, D.M. (1996). Learning Bayesian networks is NP-complete, in D. Fisher and H. Lenz (Eds.), Artificial Intelligence and Statistics, Springer-Verlag, Berlin/Heidelberg, pp. 121-130.10.1007/978-1-4612-2404-4_12Search in Google Scholar

Crawford, E., Kay, J. and Eric, M. (2002). The intelligent email sorter, Proceedings of the 19th International Conference on Machine Learning, Sydney, Australia, pp. 83-90.Search in Google Scholar

Domingos, P. and Pazzani, M. (1996). Beyond independence: Conditions for the optimality of the simple Bayesian classifier, Proceedings of the 13th International Conference on Machine Learning, Bari, Italy, pp. 105-112.Search in Google Scholar

Domingos, P. and Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss, Machine Learning (29): 103-130.10.1023/A:1007413511361Search in Google Scholar

Dougherty, J., Kohavi, R. and Sahami, M. (1995). Supervised and unsupervised discretization of continuous features, Proceedings of the 12th International Conference on Machine Learning, San Francisco, CA, USA, pp. 194-202.Search in Google Scholar

Fayyad, U.M. and Irani, K. (1993). On the handling of continuous-valued attributes in decision tree generation, Machine Learning 8: 87-102.10.1007/BF00994007Search in Google Scholar

Friedman, N., Geiger, D. and Goldszmidti, M. (1997). Bayesian network classifiers, Machine Learning 29(2): 131-163.10.1023/A:1007465528199Search in Google Scholar

Heckerman, D., Chickering, D. and Meek, C. (2004). Large sample learning of Bayesian networks is NP-hard, Journal of Machine Learning Research 5: 1287-1330.Search in Google Scholar

Kononenko, I. (2001). Machine learning for medical diagnosis: History, state of the art and perspective, Artificial Intelligence in Medicine 23: 89-109.10.1016/S0933-3657(01)00077-XSearch in Google Scholar

Langley, P., Iba, W. and Thompson, K. (1992). An analysis of Bayesian classifiers, 10th International Conference on Artificial Intelligence, San Jose, CA, USA, pp. 223-228.Search in Google Scholar

Miyahara, K. and Pazzani, M.J. (2000). Collaborative filtering with the simple Bayesian classifier, Proceedings of the 6th Pacific Rim International Conference on Artificial Intelligence, Melbourne, Australia, pp. 679-689.Search in Google Scholar

Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, San Fransisco, CA.10.1016/B978-0-08-051489-5.50008-4Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

Taheri, S., Mammadov, M. and Seifollahi, S. (2012). Globally convergent algorithms for solving unconstrained optimization problems, Optimization: 1-15.10.1080/02331934.2012.745529Search in Google Scholar

Tóth, L., Kocsor, A. and Csirik, J. (2005). On naive Bayes in speech recognition, International Journal of AppliedMathematics and Computer Science 15(2): 287-294.Search in Google Scholar

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: 1-37.10.1007/s10115-007-0114-2Search in Google Scholar

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.Search in Google Scholar

Zaidi, A., Ould Bouamama, B. and Tagina, M. (2012). Bayesian reliability models of Weibull systems: State of the art, International Journal of Applied Mathematics and Computer Science 22(3): 585-600, DOI: 10.2478/v10006-012-0045-2.10.2478/v10006-012-0045-2Search in Google Scholar

Zupan, B., Demsar, J., Kattan, M.W., Ohori, M., Graefen, M., Bohanec, M. and Beck, J.R. (2001). Orange and decisions-at-hand: Bridging predictive data mining and decision support, Proceedings of the ECML/PKDD Workshop on Integrating Aspects of Data Mining, Decision Support and Meta-Learning, Freiburg, Germany, pp. 151-162. Search in Google Scholar