Abstract
A Bayesian multinet classifier allows a different set of independence assertions among variables in each of a set of local Bayesian networks composing the multinet. The structure of the local network is usually learned using a joint-probability-based score that is less specific to classification, i.e., classifiers based on structures providing high scores are not necessarily accurate. Moreover, this score is less discriminative for learning multinet classifiers because generally it is computed using only the class patterns and avoiding patterns of the other classes. We propose the Bayesian class-matched multinet (BCM2) classifier to tackle both issues. The BCM2 learns each local network using a detection-rejection measure, i.e., the accuracy in simultaneously detecting class patterns while rejecting patterns of the other classes. This classifier demonstrates superior accuracy to other state-of-the-art Bayesian network and multinet classifiers on 32 real-world databases.
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References
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988)
Geiger, D., Heckerman, D.: Knowledge representation and inference in similarity networks and Bayesian multinets. Artificial Intelligence 82, 45–74 (1996)
Cheng, J., Greiner, R.: Learning Bayesian belief network classifiers: Algorithms and system. In: Proc. 14th Canadian Conf. on Artificial Intelligence, pp. 141–151 (2001)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29, 131–163 (1997)
Huang, K., King, I., Lyu, M.R.: Discriminative training of Bayesian Chow-Liu multinet classifier. In: Proc. Int. Joint Conf. Neural Networks, pp. 484–488 (2003)
Kontkanen, P., Myllymaki, P., Sliander, T., Tirri, H.: On supervised selection of Bayesian networks. In: Proc. 15th Conf. on Uncertainty in Artificial Intelligence, pp. 334–342 (1999)
Keogh, E.J., Pazzani, M.J.: Learning the structure of augmented Bayesian classifiers. Int. J. on Artificial Intelligence Tools 11, 587–601 (2002)
Pena, J.M., Lozano, J.A., Larranaga, P.: Learning recursive Bayesian multinets for data clustering by means of constructive induction. Machine Learning 47, 63–89 (2002)
Meila, M., Jordan, M.I.: Learning with mixtures of trees. J. of Machine Learning Research 1, 1–48 (2000)
Chow, C.K., Liu, C.N.: Approximating discrete probability distributions with dependence trees. IEEE Trans. Info. Theory 14, 462–467 (1968)
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction and Search, 2nd edn. MIT Press, Cambridge (2000)
Gurwicz, Y.: Classification using Bayesian multinets. M.Sc. Thesis. Ben-Gurion University, Israel (2004)
Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
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© 2006 Springer-Verlag Berlin Heidelberg
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Gurwicz, Y., Lerner, B. (2006). Bayesian Class-Matched Multinet Classifier. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921_15
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DOI: https://doi.org/10.1007/11815921_15
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