ICDM 2012: Advances in Data Mining. Applications and Theoretical Aspects pp 236-242 | Cite as
Decision Rules Development Using Set of Generic Operations Approach
Abstract
The main goal of presented research was to compile new approach for development learning models in a form of decision rule set. This approach devotes to using primary decision table as a primitive set of rules. Thus, each of learning cases is treated as a single classification rule. Next, a set of generic operations are applied to find the final, qualitative learning model. These generic operations are implemented in the RuleSEEKER system. During this research a few well-known algorithm for rule generation were compared with proposed solution. Obtained results are similar, sometimes even better and suggests that this method is a promising solution.
Keywords
classification rule learning algorithms rule optimizationPreview
Unable to display preview. Download preview PDF.
References
- 1.Gomuła, J., Pancerz, K., Szkoła, J.: Classification of MMPI Profiles of Patients with Mental Disorders – Experiments with Attribute Reduction and Extension. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds.) RSKT 2010. LNCS, vol. 6401, pp. 411–418. Springer, Heidelberg (2010)CrossRefGoogle Scholar
- 2.Gonzales, A., Barr, V.: Validation and verification of intelligent systems. Journal of Experimental & Theoretical Artificial Intelligence 12(2), 407–420 (2000)CrossRefGoogle Scholar
- 3.Grzymała-Busse, J.W., Hippe, Z., Knap, M., Mroczek, T.: A new algorithm for generation of decision trees. In: Nowakowski, A. (ed.) Computers in Medical Applications, Task Quarterly, vol. 8, pp. 243–247. TASK Publishing, Gdańsk (2004)Google Scholar
- 4.Grzymała-Busse, J.: A new version of the rule induction system LERS. Fundamenta Informaticae 31, 27–39 (1997)MATHGoogle Scholar
- 5.Hippe, Z.: Machine learning - a promising strategy for business information processing? In: Abramowicz, W. (ed.) Business Information Systems 1997, pp. 603–622. Academy of Economy Edit. Office, Poznan, Poland (1997)Google Scholar
- 6.Hippe, Z., Bajcar, S., Błajdo, P., Grzymała-Busse, J., Grzymała-Busse, J., Knap, M., Paja, W., Wrzesień, M.: Diagnosing skin melanoma: Current versus future directions. In: Task Quarterly, vol. 7, pp. 289–293. TASK Publishing, Gdansk (2003)Google Scholar
- 7.Hippe, Z., Hippe, T.: An attempt to automatize modeling of medical data. In: Kacki, E. (ed.) Computers in Medicine, pp. 24–31. Polish Society of Medical Informatics, Lodz, Poland (1997)Google Scholar
- 8.Ligeza, A.: Logical Foundations for Rule-Based Systems. Springer, Heidelberg (2006)MATHGoogle Scholar
- 9.Paja, W., Hippe, Z.: Feasibility Studies of Quality of Knowledge Mined from Multiple Secondary Sources. I. Implementation of Generic Operations. In: Kłopotek, M., Wierzchoń, S., Trojanowski, K. (eds.) Intelligent Information Processing and Web Mining. AISC, vol. 31, pp. 461–465. Springer, Heidelberg (2005)CrossRefGoogle Scholar
- 10.Refaeilzadeh, P., Tang, L., Liu, H.: Cross validation. In: Zsu, M.T., Liu, L. (eds.) Encyclopedia of Database Systems, pp. 27–39. Springer (2009)Google Scholar
- 11.Spreeuwenberg, S., Gerrits, R.: Requirements for successful verification in practice. In: Haller, S., Simmons, G. (eds.) Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference 2002. AAAI Press, Pensacola Beach (2002)Google Scholar
- 12.Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Elsevier, San Francisco (2005)MATHGoogle Scholar