Abstract
We describe an approach to fill missing values in decision trees during classification. This approach is derived from the ordered attribute trees method, proposed by Lobo and Numao in 2000, which builds a decision tree for each attribute and uses these trees to fill the missing attribute values. Both our approach and theirs are based on the Mutual Information between the attributes and the class. Our method takes the dependence between attributes into account by using the Mutual Information. The result of the classification process is a probability distribution instead of a single class. In this paper, we present tests performed on some real databases using our approach and Quinlan’s method. We analyse the classification results of some instances in test data and finally we discuss some perspectives.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Robnik-Sikonja, M., Kononenko, I.: Attribute Dependencies, Understandability and Split Selection in Tree Based Models. In: Bratko, I., Dzeroski, S. (eds.) Machine Learning: Proceedings of the Sixteenth International Conference ICML 1999, pp. 344–353. Morgan Kaufmann Publishers, San Francisco (1999)
Kononenko, I.: Estimating attributes: Analysis and extensions of RELIEF. In: Proceedings of the 1994 European Conference on Machine Learning, pp. 171–182 (1994)
Kira, K., Rendell, L.A.: A Practical Approach to Feature Selection. In: Sleeman, D., Edwards, J. (eds.) Proceedings of International Conference on Machine Learning, pp. 249–256. Morgan Kaufmann, San Francisco (1992)
Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. University of California, Department of Information and Computer Science, Irvine, CA (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth and Brooks (1984)
Crémilleux, B.: Induction automatique: aspects théoriques, le systéme ARBRE, Applications en médecine. Thése de doctorat, Université Joseph Fourier (1991)
Kononenko, I., Bratko, I., Roskar, E.: Experiments in Automatic Learning of Medical Diagnostic Rules, Technical Report, Jozef Stefan Institute, Ljubljana, Yugoslavia (1984)
Friedman, J.H., Kohavi, R., Yun, Y.: Lazy Decision Trees. AAAI, Menlo Park (1996)
Hawarah, L., Simonet, A., Simonet, M.: A Probabilistic Approach to Classify Incomplete Objects Using Decision Trees. In: Galindo, F., Takizawa, M., Traunmüller, R. (eds.) DEXA 2004. LNCS, vol. 3180, pp. 549–558. Springer, Heidelberg (2004)
Hawarah, L., Simonet, A., Simonet, M.: Classement d’objets incomplets dans un arbre de dcision probabiliste, Deuxime atelier sur la Fouille de donnes complexes dans un processus d’extraction des connaissances, Paris, EGC (2005)
Hawarah, L., Simonet, A., Simonet, M.: Evaluation d’une approche probabiliste pour le classement d’objets incompltement connus dans un arbre de dcision, Troisime atelier sur la Fouille de donnes complexes dans un processus d’extraction des connaissances, Lille, EGC (2006)
Lobo, O.O., Numao, M.: Ordered estimation of missing values. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS, vol. 1574, pp. 499–503. Springer, Heidelberg (1999)
Lobo, O.O., Numao, M.: Ordered estimation of missing values for propositional learning. In: Japanese Society for Artificial Intelligence, JSAI, vol. 15(1) (2000)
Lobo, O.O., Numao, M.: Suitable Domains for Using Ordered Attribute Trees to Impute Missing Values. IEICE Trans. Inf. and Syst. E84-D(2) (2001)
Quinlan, J.R.: Unknown attribute values in induction. In: Proc. Sixth International Machine Learning Workshop. Morgan Kaufmann, San Francisco (1989)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Quinlan, J.R.: Probabilistic decision trees. In: Kodratoff, Y. (ed.) Machine Learning: an Artificial Intelligence Approach, vol. 3, pp. 140–152. Morgan Kaufmann, San Mateo (1990)
Quinlan, J.R.: C4.5 Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Shannon, C.E., Weaver, W.: Théorie Mathématique de la communication, les classiques des sciences humaines (1949)
White, A.P., Liu, W.Z., Thompson, S.G., Bramer, M.A.: Techniques for Dealing with Missing Values in Classification. In: Liu, X., Cohen, P.R., Berthold, M.R. (eds.) IDA 1997. LNCS, vol. 1280, pp. 527–536. Springer, Heidelberg (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hawarah, L., Simonet, A., Simonet, M. (2006). Evaluation of a Probabilistic Approach to Classify Incomplete Objects Using Decision Trees. In: Bressan, S., Küng, J., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2006. Lecture Notes in Computer Science, vol 4080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11827405_19
Download citation
DOI: https://doi.org/10.1007/11827405_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37871-6
Online ISBN: 978-3-540-37872-3
eBook Packages: Computer ScienceComputer Science (R0)