Abstract
Rule induction has attracted a great deal of attention in Machine Learning and Data Mining. However, generating rules is not an end in itself because their applicability is not so straightforward. Indeed, the user is often overwhelmed when faced with a large number of rules.
In this paper, we propose an approach to lighten this burden when the user wishes to exploit such rules to decide which actions to do given an unsatisfactory situation. The method consists in comparing a situation to a set of classification rules. This is achieved using a suitable distance thus allowing to suggest action recommendations with minimal changes to improve that situation. We propose the algorithm Dakar for learning action recommendations and we present an application to an environmental protection issue. Our experiment shows the usefulness of our contribution in decision-making but also raises concerns about the impact of the redundancy of a set of rules in learning action recommendations of quality.
Chapter PDF
Similar content being viewed by others
Keywords
References
Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings of the 1993 ACM SIGMOD, pp. 207–216. ACM Press, New York (1993)
Cordier, M.-O.: SACADEAU: A decision-aid system to improve stream-water quality. ERCIM News. Special issue on Environmental Modelling (61), 35–36 (2005)
Duval, B., Salleb, A., Vrain, C.: Méthodes et mesures d’intérêt pour l’extraction de règles d’exception. Revue des Nouvelles Technologies de l’Information - Mesures de Qualité pour la Fouille de Données RNTI-E-1, 119–140 (2004)
Elovici, Y., Braha, D.: A decision-theoretic approach to data mining. IEEE Transactions on Systems, Man, and Cybernetics, Part A 33(1), 42–51 (2003)
He, Z., Xu, X., Deng, S.: Data Mining for Actionable Knowledge: A Survey. In: ArXiv Computer Science e-prints (January 2005)
Hutchinson, A.: Metrics on terms and clauses. In: van Someren, M., Widmer, G. (eds.) ECML 1997. LNCS, vol. 1224, pp. 138–145. Springer, Heidelberg (1997)
Ichino, M., Yaguchi, H.: Generalized minkowski metrics for mixed feature-type data analysis. IEEE Transactions on Systems, Man, and Cybernetics 24(4), 698–708 (1994)
Lavrač, N., Cestnik, B., Gamberger, D., Flach, P.: Decision support through subgroup discovery: Three case studies and the lessons learned. Machine Learning 57(1-2), 115–143 (2004)
Ling, C.X., Chen, T., Yang, Q., Cheng, J.: Mining optimal actions for profitable CRM. In: ICDM, pp. 767–770 (2002)
Liu, B., Hsu, W., Ma, Y.: Identifying non-actionable association rules. In: KDD 2001: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 329–334. ACM Press, New York (2001)
Malerba, D., Esposito, F., Gioviale, V., Tamma, V.: Comparing dissimilarity measures in symbolic data analysis. In: Joint Conferences on New Techniques and Technologies for Statistcs and Exchange of Technology and Know-how (ETK-NTTS 2001), pp. 473–481 (2001)
Mitchell, T.M.: Generalization as search. Artif. Intell. 18(2), 203–226 (1982)
Piatetsky-Shapiro, G., Matheus, C.: The interestingness of deviations. In: AAAI Workshop on Knowledge Discovery in Databases, pp. 25–36. AAAI Press, Menlo Park (1994)
Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
De Raedt, L., Van Laer, W.: Inductive constraint logic. In: ALT 1995: Proceedings of the 6th International Conference on Algorithmic Learning Theory, pp. 80–94. Springer, Heidelberg (1995)
Ramon, J., Bruynooghe, M., Van Laer, W.: Distance measures between atoms. In: CompulogNet Area Meeting on Computational Logic and Machine Learing, University of Manchester, UK, May 1998, pp. 35–41 (1998)
Ras, Z.W., Tsay, L.-S.: Discovering extended action-rules (system dear). In: IIS, pp. 293–300 (2003)
Ras, Z.W., Wieczorkowska, A.: Action-rules: How to increase profit of a company. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 587–592. Springer, Heidelberg (2000)
Sebag, M.: Delaying the choice of bias: A disjunctive version space approach. In: ICML, pp. 444–452 (1996)
Sebag, M.: Distance induction in first order logic. In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS, vol. 1297, pp. 264–272. Springer, Heidelberg (1997)
Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE Trans. on Knowledge And Data Engineering 8, 970–974 (1996)
Torgo, L.: Controlled redundancy in incremental rule learning. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol. 667, pp. 185–195. Springer, Heidelberg (1993)
Yang, Q., Yin, J., Ling, C.X., Chen, T.: Postprocessing decision trees to extract actionable knowledge. In: ICDM, pp. 685–688 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Trepos, R., Salleb, A., Cordier, MO., Masson, V., Gascuel, C. (2005). A Distance-Based Approach for Action Recommendation. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds) Machine Learning: ECML 2005. ECML 2005. Lecture Notes in Computer Science(), vol 3720. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564096_41
Download citation
DOI: https://doi.org/10.1007/11564096_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29243-2
Online ISBN: 978-3-540-31692-3
eBook Packages: Computer ScienceComputer Science (R0)