Abstract
One of the main factors for the knowledge discovery success is related to the comprehensibility of the patterns discovered by applying data mining techniques. Amongst which we can point out the Bayesian networks as one of the most prominent when considering the easiness of knowledge interpretation achieved. Bayesian networks, however, present limitations and disadvantages regarding their use and applicability. This paper presents an extension for the improvement of Bayesian networks, incorporating models of multiple regression for structure learning.
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References
Huang, H., Song, H., Tian, F., Lu, Y., e Wang, Q.: A comparatively research in incremental learning of Bayesian networks. Intelligent Control and Automation. Fifth World Congress on 5, 4260–4264 (2004)
Morales, M.M., Dominguez, R.G., Ramirez, N.C., Hernandez, A.G., e Andrade, J.L.J.: A method based on genetic algorithms and fuzzy logic to induce Bayesian networks, Computer Science, 2004. In: ENC 2004. Proceedings of the Fifth Mexican International Conference, pp. 176–180 (2004)
Larrañaga, P.: Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters. IEEE Journal on Pattern Analysis and Machine Intelligence 18(9), 912–926 (1996)
Rissanen, J.: Modeling by shortest data description. Automatica 14, 465–471 (1978)
Akaike, H.: A new look at the statistical model identification. IEEE Transactions on Automatic Control 19(6), 716–723 (1974)
Morales, M.M., Ramírez, N.C., Andrade, J.L.J., e Domínguez, R.G.: Bayes-N: an algorithm for learning Bayesian networks from data using local measures of information gain applied to classification problems. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds.) MICAI 2004. LNCS (LNAI), vol. 2972, Springer, Heidelberg (2004)
Ramírez, N.C.: Building Bayesian networks from data: a constraint based approach, PhD Thesis, University of Sheffield (2001)
Spirtes, P.R., Shcheines, R., e Clark, G.: TETRAD II: Tools for Discovery. Lawrence Erlbaum Associates, Hillsdale, NJ., USA (1994)
Cooper, G., e Herskovitz, E.: A Bayesian Method for the Induction of Probabilistic Networks from Data. Machine Learning 9, 309–347 (1992)
Shetty, S., Song, M.: Structure learning of Bayesian networks using a semantic genetic algorithm-based approach. In: ITRE 2005. 3rd International Conference on Information Technology: Research and Education, pp. 454–458 (2005)
Li, G., Tong, F., Dai, H.: Evolutionary structure learning algorithm for Bayesian network and Penalized Mutual Information metric, Data Mining. In: ICDM 2001. Proceedings IEEE International Conference, pp. 615–616 (2001)
Li, X.-L., Yuan, S.-M., He, X.-D.: Learning Bayesian networks structures based on extending evolutionary programming, In: Machine Learning and Cybernetics. Proceedings of 2004 International Conference, vol. 3, pp. 1594–1598 (2004)
Gamez, J.A., de Campos, L.M., Moral, S.: Partial abductive inference in Bayesian belief networks - an evolutionary computation approach by using problem-specific genetic operators. Evolutionary Computation, IEEE Transactions 6(2), 105–131 (2002)
Handa, H., Katai, O.: Estimation of Bayesian network algorithm with GA searching for better network structure. In: Neural Networks and Signal Processing. Proceedings of the 2003 International Conference, vol. 1, pp. 436–439 (2003)
Peng, H., Ding, C.: Structure search and stability enhancement of Bayesian networks, Data Mining. In: ICDM 2003. Third IEEE International Conference, pp. 621–624. IEEE Computer Society Press, Los Alamitos (2003)
Chickering, D.M., Heckerman, D., Meek, C.: Large-Sample Learning of Bayesian Networks is NP-Hard. Journal of Machine Learning Research 5, 1287–1330 (2004)
Cheng, J., Bell, D., e Liu, W.: Learning Bayesian Networks from Data: An Efficient Approach Based on Information Theory. Artificial Intelligence. 137(1-2), 43–90 (2002)
Robinson, R.W.: Counting unlabeled acyclic digraphs. In: Proceedings of the Fifth Australian Conference on Combinatorial Mathematics, pp. 28–43 (1976)
Herskovits, E.: Computer-Based Probabilistic Networks Construction, Ph.D. Thesis, Medical Information Sciences, University of Pittsburgh (1991)
Hair, J.F.J., Anderson, R.E., Tatham, R.L., e Black, W.C.: Multivariate data analysis. Prentice-Hall, Englewood Cliffs (1998)
Rice, J.A.: Mathematical Statistics and Data Analysis, 2nd edn. Duxbury Press, Boston, MA (1995)
Santana, A.C.: Quantitative Methods in Econometry, UFRA (2003)
Lauritzen, S.L., e Spiegelhalter, D.J.: Local computations with probabilities on graphical structures and their application to expert systems. Royal Statistics Society B50(2), 157–194 (1988)
Yang, S., e Chang, K.: Comparison of Score Metrics for Bayesian Network Learning. IEEE Transactions on Systems, Man, and Cybernetics Part A32, 419–428 (2002)
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de Santana, Á.L., Francês, C.R.L., Costa, J.C.W. (2007). Algorithm for Graphical Bayesian Modeling Based on Multiple Regressions. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_47
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DOI: https://doi.org/10.1007/978-3-540-76631-5_47
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