Mining breast cancer data with XCS

Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

Volumn , Issue , 2007, Pages 2066-2073

  Kharbat, Faten ^a   Bull, Larry ^b   Odeh, Mohammed ^b  

^a ZARQA UNIVERSITY   (Jordan)

^b UNIVERSITY OF THE WEST OF ENGLAND   (United Kingdom)

Author keywords

Classification; Genetic algorithm; Learning classifier system; Medical informatics

Indexed keywords

CLASSIFICATION (OF INFORMATION); GENETIC ALGORITHMS; MEDICAL EDUCATION; ONCOLOGY;

BREAST CANCER; LEARNING CLASSIFIER SYSTEM; XCS;

DATA MINING;

EID: 34548137365     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1276958.1277362     Document Type: Conference Paper

References (38)

1. Bacardit, J., & Butz, M. (2004). Data Mining in Learning Classifier Systems: Comparing XCS with GAssis. Advances in Learning Classifier Systems (7th International Workshop, IWLCS 2004), Seattle, USA, LNAI, Springer.
2. Batista, G., Prati, R., & Monard, M. (2004). A study of the behaviour of several methods for balancing machine learning training data. SIGKDD Explorations, 6(1), pp 20-29.
3. Bernado, E., Llora, X., & Garrell, J. (2002). XCS and GALE: a Comparative Study of Two Learning Classifier Systems on Data Mining. Advances in Learning Classifier Systems, 4th International Workshop, volume 2321 of Lecture Notes in Artificial Intelligence, Springer, pp 115-132.
4. Blake, C., & Merz, C. (1998). UCI Repository of Machine Learning Databases [online], Irvine, CA: University of California, Department of Information and Computer Science. Available from: http://www.ics.uci.edu/ ~mlearn/MLRepository.html [Accessed 2/2004].
5. th International Conference on Genetic Algorithms, pp. 288-295, Morgan Kauffman.
6. Butz, M. & Wilson, S.W. (2001) An Algorithmic Description of XCS. In Advances in Learning Classifier Systems: Proceedings of the Third International Conference - IWLCS2000. Springer, pp. 253-272.
7. Famili, F., Shen, W., Weber, R., & Simoudis, E. (1997). Data Preprocessing and Intelligent Data Analysis. Intelligent. Data Analysis, 1(1-4), pp 3-23.
8. Holland, J.H. (1986). Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In Michalski, Carbonell, & Mitchell (eds) Machine learning, an artificial intelligence approach. Morgan Kaufmann.
9. Holmes, J. (1997). Discovering risk of disease with a learning classifier system, In: T. Baeck (ed). Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA97). Morgan Kaufmann, San Francisco, CA (1997).
10. Holmes, J. (2000). Learning Classifier Systems Applied to Knowledge Discovery in Clinical Research Databases. In: P. Lanzi, W. Stolzmann, S. W. Wilson (eds). Learning Classifier Systems: From Foundations to Applications. Springer-Verlag Heidelberg, pp 243-261
11. Holmes J., Bilker W., (2002) The Effect of Missing Data on Learning Classifier System Learning Rate and Classification Performance. IWLCS 2002: 46-60.
12. Holmes, J., Sager, J., & Bilker, W. (2004). A Comparison of Three Methods for Covering Missing Data in XCS. Seventh International Workshop on Learning Classifier Systems (IWLCS-2004).
13. Japkowicz, N. (2003). Class Imbalances: Are we focusing on the Right Issue? Notes from the ICML Workshop on Learning from Imbalanced Data Sets II.
14. Japkowicz, N., and Stephen, S.(2002) The class imbalance problem: a systematic study. Intelligent Data Analysis, 6(5):429-450.
15. Japkowicz, N. (2000). The class imbalance problem: Significance and strategies. In Proceedings of the 2000 International Conference on Artificial Intelligence (ICAI '2000).
16. Jensen, F. (1996). An Introduction to Bayesian Networks. Springer.
17. Jo, T., & Japkowicz, N. (2004). Class Imbalances versus Small Disjoints, SIGKDD Explorations,6(1).
18. Kharbat, F. (2006) Learning Classifier Systems for Knowledge Discovery in Breast Cancer. PhD Dissertation. University of the West of England, U.K.
19. Kharbat, F., Bull, L. & Odeh, M. (2005) Revisiting Genetic Selection in the XCS Learning Classifier System. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE, pp2061-2068.
20. Kohavi R. & Provost F. (1998)., Glossary of Terms. Editorial for the Special Issue on Applications of Machine Learning and the Knowledge Discovery Process. 30: 2/3.
21. Liu, W., Shen, P., Qu, Y., & Xia, D. (2001). Fast algorithm of support vector machines in lung cancer diagnosis. International Workshop on Medical Imaging and Augmented Reality, 2001, pp 188-192.
22. Moore, A., & Hoang, A. (2002). A performance assessment of Bayesian networks as a predictor of breast cancer survival. Second international workshop on Intelligent systems design and application, pp 3-8.
23. Ogunyemi, O., Chlebowski, R., Matloff, E., Schnabel, F., Orr, R., & Col, N. (2004). Creating Bayesian Network Models for Breast Cancer Risk Prediction. Cancer Risk Prediction Models: A Workshop on Development, Evaluation, and Application, pp 20-21.
24. Quinlan, J. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA.
25. Sierra, B., & Larrannaga, P. (1998). Predicting the survival in malignant skin melanoma using Bayesian networks. An empirical comparison between different approaches, Artificial Intelligence in Medicine, 14(1-2), pp 215-230.
26. Sorace, J., & Zhan, M. (2003). A data review and reassessment of ovarian cancer serum proteomic profiling. BMC Bioinformatics 4(24).
27. Tan, K., Yu, Q., Heng, C., & Lee, T. (2003). Evolutionary computing for knowledge discovery in medical diagnosis, Artificial Intelligence in Medicine, 27(2), pp 129-154.
28. Wilson, S. (2001). Compact Rulesets from XCSI. Fourth International Workshop on Learning Classifier Systems (IWLCS-2001). pp. 197-210, San Francisco, CA
29. Wilson, S., (1995) Classifier Fitness Based on Accuracy. Evolutionary Computing 3: 149-175.
30. Witten, I., & Frank, E., (2005). Data Mining: Practical machine learning tools and techniques, 2nd Edition. Morgan Kaufmann, San Francisco
31. Wyatt, D., Bull, L., & Parmee, I. (2004). Building Compact Rulesets for Describing Continuous-Valued Problem Spaces Using a Learning Classifier System. In: I. Parmee (ed). Adaptive Computing in Design and Manufacture VI. Springer, pp 235-248.
32. Prati, R., Batista, G., and Monard, M. (2004) Class Imbalances versus Class Overlapping: an Analysis of a Learning System Behavior. In MICAI, pp. 312-321.
33. Barandela, R., Valdovinos, R., Scnchez, J., & Ferri, F. (2004). The imbalanced training sample problem: Under or over sampling?. Joint IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition (SSPR/SPR'04). Lecture Notes in Computer Science 3138, Springer-Verlag, pp 806-814.
34. Platt, J. (1998). Fast Training of Support Vector Machines using Sequential Minimal Optimization. Advances in Kernel Methods - Support Vector Learning In: B. Schoelkopf, C Burges, and A. Smola (eds). MIT Press, pp 185-208.
35. Fu, C., & Davis, L. (2002). A Modified Classifier System Compaction Algorithm. Genetic and evolutionary computation conference (GECCO-2002), pp 920-925.
36. Dixon, P., Corne, D., & Oates, M. (2003). A Ruleset Reduction Algorithm for the XCS Learning Classifier System. In: P. Lanzi, W. Stolzmann, and S. Wilson (eds.) Proceedings of the 3rd International Workshop on Learning Classifier Systems. Springer LNCS, pp 20-29.
37. Butz, M., Goldberg, D., & Tharakunnel, K. (2003).
38. Analysis and improvement of fitness exploration in XCS: bounding models, Tournament selection and bilateral accuracy. Evolutionary Computation, 11(3), pp 239-277.