Abstract
A constraint is a relation with an active behavior. For a given relation, we propose to learn a representation adapted to this active behavior. It yields two contributions. The first is a generic meta-technique for classifier improvement showing performances comparable to boosting. The second lies in the ability of using the learned concept in constraint-based decision or optimization problems. It opens a new way of integrating Machine Learning in Decision Support Systems.
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References
Abdennadher, S., Rigotti, C.: Automatic generation of rule-based constraint solvers over finite domains. ACM TOCL 5(2) (2004)
Apt, K.R.: Principles of Constraint Programming. Cambridge University Press, Cambridge (2003)
Apt, K.R., Monfroy, E.: Automatic generation of constraint propagation algorithms for small finite domains. In: Jaffar, J. (ed.) CP 1999. LNCS, vol. 1713, pp. 58–72. Springer, Heidelberg (1999)
Bessière, C., Régin, J.-C.: Arc-consistency for general constraint networks: preliminary results. In: IJCAI, Nagoya, Japan, pp. 398–404. Morgan Kaufmann, San Francisco (1997)
Freund, Y., Shapire, R.: A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence 14(5), 771–780 (1999)
Lallouet, A., Dao, T.-B.-H., Legtchenko, A., Ed-Dbali, A.: Finite domain constraint solver learning. In: Gottlob, G. (ed.) International Joint Conference on Artificial Intelligence, Acapulco, Mexico, pp. 1379–1380. AAAI Press, Menlo Park (2003)
Lavrac, N., Dzeroski, S.: Inductive Logic Programming: Techniques and Applications. Ellis Horwood (1994)
Moore, R.E.: Interval Analysis. Prentice Hall, Englewood Cliffs (1966)
Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Rossi, F., Sperduti, A.: Acquiring both constraint and solution preferences in interactive constraint system. Constraints 9(4) (2004)
RuleQuest Research. See5: An informal tutorial (2004), http://www.rulequest.com/see5-win.html
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. Parallel Distributed Processing 1, 318–362 (1986)
Tsoumakas, G., Katakis, I., Vlahavas, I.P.: Effective voting of heterogeneous classifiers. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 465–476. Springer, Heidelberg (2004)
Young, R.C.: The algebra of multi-valued quantities. Mathematische Annalen 104, 260–290 (1931)
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Lallouet, A., Legtchenko, A. (2005). Two Contributions of Constraint Programming to Machine Learning. 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_61
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DOI: https://doi.org/10.1007/11564096_61
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