Abstract
In the traditional na\”ıve Bayes classification method, training data are represented as a single table (or database relation), where each row corresponds to an example and each column to a predictor variable or a target variable. In this paper we propose a multi-relational extension of the na\”ıve Bayes classification method that is characterized by three aspects: first, an integrated approach in the computation of the posterior probabilities for each class; second, the applicability to both discrete and continuous attributes; third, the consideration of knowledge on the data model embedded in the database schema during the generation of classification rules. The proposed method has been implemented in the new system Mr-SBC and tested on three benchmark tasks. Results on predictive accuracy favour our system for the most complex task. Mr-SBC also proved to be an efficient multi-relational data mining system with a tight dose integration to a relational DBMS.
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References
Blockeel, H.: Top-down induction of first order logical decision trees. PhD thesis, Department of Computer Science, Katholieke Universiteit Leuven (1998)
De Raedt, L.: Attribute-value learning versus inductive logic programming: the missing links. In: Proceedings of the 8th International Conference on Inductive Logic Programming, pp. 128–137. Springer, Heidelberg (1998)
Domingos, P., Pazzani, M.: On the optimality of the simple bayesian classifier under zero-one loss. Machine Learning 29(2-3), 103–130 (1997)
Dougherty, J., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of continuous features. In: Proceedings of the 12th International Conference on Machine Learning, pp. 194–202 (1995)
Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Proc. Of the 13th International Joint Conference on Artificial Intelligence, pp. 1022–1027 (1994)
Flach, P., Lachiche, N.: First-order bayesian classification with 1BC (submitted) (2000), http://hydria.u-strasbg.fr/lachiche/1bc.ps.gz
Flach, P.A., Lachiche, N.: Confirmation-guided discovery of first-order rules with tertius. Machine Learning 42(1/2), 61–95 (2000)
Flach, P.A., Lachiche, N.: Decomposing probability distributions on structured individuals. In: Brito, P., Costa, J. (eds.) Proceedings of the ECML 2000 workshop on Dealing with Structured Data in Machine Learningand Statistics, Barcelona, Spain, pp. 33–43 (2000)
Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: Morgan Kaufman (ed.) Proceedings of the 6th International Joint Conference on Artificial Intelligence, pp. 1300–1309 (1999)
Getoor, L.: Multi-relational data mining using probabilistic relational models: research summary. In: Knobbe, A.J., van der Wallen, D.M.G. (eds.) Proceedings of the 1st Workshop in Multi-relational Data Mining, pp. 1300–1309 (2001)
Getoor, L., Koller, D., Taskar, B.: Statistical models for relational data. In: Proceedings of the KDD-2002 Workshop on Multi-Relational Data Mining, Edmonton, CA, pp. 36–55 (2002)
Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Machine Learning 11, 63–90 (1993)
Krogel, M., Wrobel, S.: Transformation-based learning using multirelational aggregation. In: Rouveirol, C., Sebag, M. (eds.) ILP 2001. LNCS (LNAI), vol. 2157, p. 142. Springer, Heidelberg (2001)
Leiva, H.A.: MRDTL: A multi-relational decision tree learning algorithm. PhD thesis, Master thesis, University of Iowa (2002)
Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)
Muggleton, S.H., Bain, M., Hayes-Michie, J., Michie, D.: An experimental comparison of human and machine learning formalisms. In: Proc. 6th International Workshop on Machine Learning, San Mateo, CA, pp. 113–118. Morgan Kaufmann, San Francisco (1989)
Pompe, U., Kononenko, I.: Linear space induction in first order logic with relief. In: Viertl, R., Della, G., Kruse, R.R. (eds.) CISM Lecture Notes, Udine, Italy (1994)
Pompe, U., Kononenko, I.: Naive bayesian classifier within ILP-R. In: De Raedt, L. (ed.) Proc. of the 5th Int. Workshop on Inductive Logic Programming. Dept. of Computer Science, pp. 417–436. Katholieke Universiteit Leuven (1995)
Srinivasan, A., Kingand, R.D., Muggleton, S.: The role of background knowledge: using a problem from chemistry to examine the performance of an ilp program (1999)
Wrobel, S., Džeroski, S., Lavrac, N.: Relational Data Mining chapter Inductive logic programming for knowledge discovery in databases, pp. 74–101. Springer, Heidelberg (2001)
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Ceci, M., Appice, A., Malerba, D., Colonna, V. (2003). Multi-relational Structural Bayesian Classifier. In: Cappelli, A., Turini, F. (eds) AI*IA 2003: Advances in Artificial Intelligence. AI*IA 2003. Lecture Notes in Computer Science(), vol 2829. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39853-0_21
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DOI: https://doi.org/10.1007/978-3-540-39853-0_21
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