The class imbalance problem in UCS classifier system: A preliminary study

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4399 LNAI, Issue , 2007, Pages 161-180

  Orriols Puig, Albert ^a   Bernadó Mansilla, Ester ^a  

^a UNIVERSITAT RAMON LLULL   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; DATA STRUCTURES; PROBLEM SOLVING;

CLASSIFICATION PROBLEMS; DATASETS;

LEARNING SYSTEMS;

EID: 38049103501     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-71231-2_12     Document Type: Conference Paper

References (28)

1. A. Orriols Puig and E. Bernadó Mansilla. The Class Imbalance Problem in UCS Classifier System: Fitness Adaptation. In Congress on Evolutionary Computation, volume 1, pages 604-611, Edinburgh, UK, 2-5 September 2005. IEEE.
2. J. Bacardit. 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.
3. J. Bacardit and M.V. Butz. Data Mining in Learning Classifier Systems: Comparing XCS with GAssist. In Seventh International Workshop on Learning Classifier Systems (IWLCS-2004), 2004.
4. E. Bernadó Mansilla. Complejidad del Aprendizaje y Muestreo de Ejemplos en Sistemas Clasificadores. In Tercer Congreso Espaol de Metaheursticas, Algoritmos Evolutivos y, Bioinspirados (MAEB'2004), pages 203-210, 2004.
5. M.V. Butz. 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.
6. N. Chawla, K. Bowyer, L Hall, and W. Kegelmeyer. SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16:321-357, 2002.
7. E. Bernadó Mansilla and J.M. Garrell Guiu. Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks. Evolutionary Computation, 11(3):209-238, 2003.
8. E. Bernadó Mansilla and T.K. Ho. Domain of Competence of XCS Classifier System in Complexity Measurement Space. IEEE Transactions on Evolutionary Computation, 9(1):82-104, 2005.
9. R.E. Fawcett and F. Provost. Adaptive Fraud Detection. Data Mining and Knowledge Discovery, 3(1):291-316, 1997.
10. G.M. Weiss. 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.
11. J.H. Holland. Adaptation in Natural and Artificial Systems. The University of Michigan Press, 1975.
12. J.H. Holland. Adaptation. In R. Rosen and F. Snell, editors, Progress in Theoretical Biology, volume 4, pages 263-293. New York: Academic Press, 1976.
13. J.H. Holmes. 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, pages 635-642. Morgan Kaufmann, 1998.
14. T. Kovacs. Deletion Schemes for Classifier Systems. In Wolfgang Banzhaf, Jason Daida, A.E. Eiben, Max H. Garzón, Vasant Honavar, Mark Jakiela, and Robert E Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, (GECCO-99), pages 329-336. Morgan Kaufmann, 1999.
15. T. Kovacs and M. Kerber. What makes a problem hard for XCS. In Lanzi, P. L., Stolzmann, W., & Wilson, S. W. (Eds.), Advances in Learning Classifier Systems: Third International Workshop, IWLCS, pages 80-99. Springer-Verlag, 2000.
16. P.L. Lanzi. A study of the generalization capabilities of XCS. In Thomas Bäck, editor, Proc. of the Seventh Int. Conf. on Genetic Algorithms, pages 418-425, San Francisco, CA, 1997. Morgan Kaufmann.
17. M. V. Butz, D. E. Golberg, and P. L. Lanzi. Bounding Learning Time in XCS. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'2004), Berlin, 2004. Springer Verlag.
18. E. Bernadó Mansilla. Contributions to Genetic Based Classifier Systems. PhD thesis, Enginyeria i Arquitectura la Salle, Ramon Llull University, Barcelona, 2002.
19. E. Bernadó Mansilla, F.X. Llorà Fàbrega, and J.M. Garrell Guiu. XCS and GALE: a Comparative Study of Two Learning Classifier Systems on Data Mining. In P.L. Lanzi, W. Stolzmann, and S.W. Wilson, editors, Advances in Learning Classifier Systems, 4th International Workshop, volume 2321 of Lecture Notes in Artificial Intelligence, pages 115-132. Springer, 2002.
20. P.M. Murphy and D.W. Aha. UCI Repository of machine learning databases, University of California at Irvine, Department of Information and Computer Science, 1994.
21. M.V. Butz and S.W. Wilson. An algorithmic description of XCS. In P.L. Lanzi, W. Stolzmann, and S.W. Wilson, editors, Advances in Learning Classifier Systems: Proceedings of the Third International Workshop, volume 1996 of Lecture Notes in Artificial Intelligence, pages 253-272. Springer, 2001.
22. N. Japkowicz and S. Stephen. The Class Imbalance Problem: Significance and Strategies. In 2000 International Conference on Artificial Intelligence (ICAI'2000), volume 1, pages 111-117, 2000.
23. N. Japkowicz and S. Stephen. The Class Imbalance Problem: A Systematic Study. Intelligent Data Analisis, 6(5):429-450, November 2002.
24. R. Giráldez, J.S. Aguilar-Ruiz, and J.E. Riquelme. Evolutionary Learning of Hierarchical Decision Rules. IEEE Transactions on Systems, Man, and Cybernetics, 33(2):324.331, 2003.
25. R.E. Schapire. A Brief Introduction to Boosting. In Sixteenth International Joint Conference on Artificial Intelligence, 1999.
26. C. Stone and L. Bull. For Real! XCS with Continuous-Valued Inputs. Evolutionary Computation, 11(3):299-336, 2003.
27. S.W. Wilson. Generalization in the XCS Classifier System. In J.R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D. Fogel, M.H. Garzon, D.E. Goldberg, H. Iba, and R. Riolo, editors, Genetic Programming: Proceedings of the Third Annual Conference, pages 665-674. Morgan Kaufmann, 1998.
28. S.W. Wilson. Classifier Fitness Based on Accuracy. Evolutionary Computation, 3(2):149-175, 1995.