Abstract
This paper highlights the interest of implicative statistics for classification trees. We start by showing how Gras’ implication index may be defined for the rules derived from an induced decision tree. Then, we show that residuals used in the modeling of contingency tables provide interesting alternatives to Gras’ index. We then consider two main usages of these indexes. The first is purely descriptive and concerns the a posteriori individual evaluation of the classification rules. The second usage, considered for instance by [15], relies upon the intensity of implication to define the conclusion in each leaf of the induced tree.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agresti, A.: Categorical Data Analysis. Wiley, New York (1990)
Bishop, Y.M.M., Fienberg, S.E., Holland, P.W.: Discrete Multivariate Analysis. MIT Press, Cambridge (1975)
Blanchard, J., Guillet, F., Gras, R., Briand, H.: Using information-theoretic measures to assess association rule interestingness. In: Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), pp. 66–73. IEEE Computer Society, Los Alamitos (2005)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification And Regression Trees. Chapman and Hall, New York (1984)
Briand, H., Fleury, L., Gras, R., Masson, Y., Philippe, J.: A statistical measure of rules strength for machine learning. In: Proceedings of the Second World Conference on the Fundamentals of Artificial Intelligence WOCFAI 1995, Paris, Angkor, pp. 51–62 (1995)
Gras, R.: Contribution á l’étude expérimentale et á l’analyse de certaines acquisitions cognitives et de certains objectifs didactiques. Thése d’état, Université de Rennes 1, France (1979)
Gras, R., Couturier, R., Blanchard, J., Briand, H., Kuntz, P., Peter, P.: Quelques critéres pour une mesure de qualité de régles d’association. Revue des nouvelles technologies de l’information RNTI E-1, 3–30 (2004)
Gras, R., Larher, A.: L’implication statistique, une nouvelle méthode d’analyse de données. Mathématique, Informatique et Sciences Humaines 120, 5–31 (1992)
Gras, R., Ratsima-Rajohn, H.: L’implication statistique, une nouvelle méthode d’analyse de données. RAIRO Recherche Opérationnelle 30(3), 217–232 (1996)
Guillaume, S., Guillet, F., Philippé, J.: Improving the discovery of association rules with intensity of implication. In: Żytkow, J.M. (ed.) PKDD 1998. LNCS, vol. 1510, pp. 318–327. Springer, Heidelberg (1998)
Kass, G.V.: An exploratory technique for investigating large quantities of categorical data. Applied Statistics 29(2), 119–127 (1980)
Lerman, I.C., Gras, R., Rostam, H.: Elaboration d’un indice d’implication pour données binaires I. Mathématiques et sciences humaines (74), 5–35 (1981)
Raftery, A.E.: Bayesian model selection in social research. In: Marsden, P. (ed.) Sociological Methodology, pp. 111–163. The American Sociological Association, Washington (1995)
Suzuki, E., Kodratoff, Y.: Discovery of surprising exception rules based on intensity of implication. In: Żytkow, J.M. (ed.) PKDD 1998. LNCS, vol. 1510, pp. 10–18. Springer, Heidelberg (1998)
Zighed, D.A., Rakotomalala, R.: Graphes d’induction: apprentissage et data mining. Hermes Science Publications, Paris (2000)
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
Ritschard, G., Zighed, D.A. (2006). Implication Strength of Classification Rules. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds) Foundations of Intelligent Systems. ISMIS 2006. Lecture Notes in Computer Science(), vol 4203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875604_53
Download citation
DOI: https://doi.org/10.1007/11875604_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45764-0
Online ISBN: 978-3-540-45766-4
eBook Packages: Computer ScienceComputer Science (R0)