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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-540-39853-0_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20119-9
Online ISBN: 978-3-540-39853-0
eBook Packages: Springer Book Archive