Abstract
The class imbalance problem has been said recently to hinder the performance of learning systems. In fact, many of them are designed with the assumption of well-balanced datasets. But this commitment is not always true, since it is very common to find higher presence of one of the classes in real classification problems. The aim of this paper is to make a preliminary analysis on the effect of the class imbalance problem in learning classifier systems. Particularly we focus our study on UCS, a supervised version of XCS classifier system. We analyze UCS’s behavior on unbalanced datasets and find that UCS is sensitive to high levels of class imbalance. We study strategies for dealing with class imbalances, acting either at the sampling level or at the classifier system’s level.
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Puig, A.O., Mansilla, E.B.: The Class Imbalance Problem in UCS Classifier System: Fitness Adaptation. In: Congress on Evolutionary Computation, vol. 1, Edinburgh, UK, 2-5 September 2005, pp. 604–611. IEEE Computer Society Press, Los Alamitos (2005)
Bacardit, J.: Pittsburgh genetic-based machine learning in the data mining era: representations, generalization and run-time. PhD thesis, Department of Computer Science. Enginyeria i Arquitectura la Salle, Ramon Llull University, Barcelona (2004)
Bacardit, J., Butz, M.V.: Data Mining in Learning Classifier Systems: Comparing XCS with GAssist. In: Seventh International Workshop on Learning Classifier Systems (IWLCS-2004) (2004)
Mansilla, E.B.: Complejidad del Aprendizaje y Muestreo de Ejemplos en Sistemas Clasificadores. In: Tercer Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB’2004), pp. 203–210 (2004)
Butz, M.V.: Rule-based Evolutionary Online Learning Systems: Learning Bounds, Classification and Prediction. PhD thesis, Illinois Genetic Algorithms Laboratory (IlliGAL) - University of Illinois at Urbana Champaign, Urbana Champaign 117 (2004)
Chawla, N., et al.: SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research 16, 321–357 (2002)
Mansilla, E.B., Guiu, J.M.G.: Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks. Evolutionary Computation 11(3), 209–238 (2003)
Mansilla, E.B., Ho, T.K.: Domain of Competence of XCS Classifier System in Complexity Measurement Space. IEEE Transactions on Evolutionary Computation 9(1), 82–104 (2005)
Fawcett, R.E., Provost, F.: Adaptive Fraud Detection. Data Mining and Knowledge Discovery 3(1), 291–316 (1997)
Weiss, G.M.: The effect of small disjunct and class distribution on decision tree learning. PhD thesis, Graduate School - New Brunswick. Rutgers, The State University of New Jersey, New Brunswick, New Jersey (2003)
Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)
Holland, J.H.: Adaptation. In: Rosen, R., Snell, F. (eds.) Progress in Theoretical Biology, vol. 4, pp. 263–293. Academic Press, London (1976)
Holmes, J.H.: Differential negative reinforcement improves classifier system learning rate in two-class problems with unequal base rates. In: Genetic Programming 1998: Proceedings of the Third Annual Conference, pp. 635–642. Morgan Kaufmann, San Francisco (1998)
Kovacs, T.: Deletion Schemes for Classifier Systems. In: Banzhaf, W., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99), pp. 329–336. Morgan Kaufmann, San Francisco (1999)
Kovacs, T., Kerber, M.: What makes a problem hard for XCS. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS (LNAI), vol. 1996, pp. 80–99. Springer, Heidelberg (2001)
Lanzi, P.L.: A study of the generalization capabilities of XCS. In: Bäck, T. (ed.) Proc. of the Seventh Int. Conf. on Genetic Algorithms, pp. 418–425. Morgan Kaufmann, San Francisco (1997), citeseer.ist.psu.edu/lanzi97study.html
Butz, M.V., Golberg, D.E., Lanzi, P.L.: Bounding Learning Time in XCS. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, Springer, Heidelberg (2004)
Mansilla, E.B.: Contributions to Genetic Based Classifier Systems. PhD thesis, Enginyeria i Arquitectura la Salle, Ramon Llull University, Barcelona (2002)
Mansilla, E.B., Llorà Fàbrega, F.X., Garrell Guiu, J.M.: XCS and GALE: a Comparative Study of Two Learning Classifier Systems on Data Mining. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2001. LNCS (LNAI), vol. 2321, pp. 115–132. Springer, Heidelberg (2002)
Murphy, P.M., Aha, D.W.: UCI Repository of machine learning databases, University of California at Irvine, Department of Information and Computer Science (1994)
Butz, M.V., Wilson, S.W.: An algorithmic description of XCS. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS (LNAI), vol. 1996, pp. 253–272. Springer, Heidelberg (2001)
Japkowicz, N., Stephen, S.: The Class Imbalance Problem: Significance and Strategies. In: 2000 International Conference on Artificial Intelligence (IC-AI’2000), vol. 1, pp. 111–117 (2000)
Japkowicz, N., Stephen, S.: The Class Imbalance Problem: A Systematic Study. Intelligent Data Analisis 6(5), 429–450 (2002)
Giráldez, R., Aguilar-Ruiz, J.S., Riquelme, J.E.: Evolutionary Learning of Hierarchical Decision Rules. IEEE Transactions on Systems, Man, and Cybernetics 33(2), 324 (2003)
Schapire, R.E.: A Brief Introduction to Boosting. In: Sixteenth International Joint Conference on Artificial Intelligence (1999)
Stone, C., Bull, L.: For Real! XCS with Continuous-Valued Inputs. Evolutionary Computation 11(3), 299–336 (2003)
Wilson, S.W.: Generalization in the XCS Classifier System. In: Koza, J.R., et al. (eds.) Genetic Programming: Proceedings of the Third Annual Conference, pp. 665–674. Morgan Kaufmann, San Francisco (1998)
Wilson, S.W.: Classifier Fitness Based on Accuracy. Evolutionary Computation 3(2), 149–175 (1995)
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Orriols-Puig, A., Bernadó-Mansilla, E. (2007). The Class Imbalance Problem in UCS Classifier System: A Preliminary Study. In: Kovacs, T., Llorà, X., Takadama, K., Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Learning Classifier Systems. IWLCS IWLCS IWLCS 2003 2004 2005. Lecture Notes in Computer Science(), vol 4399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71231-2_12
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