Rough Classifiers Sensitive to Costs Varying from Object to Object
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We present modification of the ProbRough algorithm for inducing decision rules from data. The generated rough classifiers are now sensitive to costs varying from object to object in the training data. The individual costs are represented by new cost attributes defined for every single decision. In this approach the decision attribute is dispensable. Grouping of objects and defining prior probabilities are made on the basis of the group attribute. Values of this attribute may have no relations with the decisions. The proposed approach is a generalization of the methodology incorporating the cost matrix. Behavior of the algorithm is illustrated on the data concerning the credit evaluation task.
KeywordsLearning Object Average Cost Group Attribute Cost Matrix Cost Criterion
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- 1.Ezawa, K. J., Singh, M., Norton, S. W. (1996). Learning goal oriented networks for telecommunications risk management. Proceedings of the 13th International Conference on Machine Learning, Bari, Italy, July 3–6, 1996. Avaliable at ftp://ftp.cis.upenn.edu/pub/msingh/ml96_alt.ps.Z
- 3.Pazzani, M., Merz, C., Murphy, P., Ali, K., Hume, T., Brunk, C. (1994). Reducing misclassification costs. Proceedings of the 11th International Conference on Machine Learning (pp. 217–225). Morgan Kaufmann.Google Scholar
- 4.Piasta, Z., Lenarcik, A. (1996), Rule induction with probabilistic rough classifiers. ICS Research Report 24/96, Warsaw University of Technology, to appear in Machine Learning.Google Scholar
- 5.Piasta, Z., Lenarcik, A. (1998), Learning rough classifiers from large databases with missing values. In: L. Polkowski, A. Skowron (eds): Rough Sets in Knowledge Discovery. Physica-Verlag (Springer), forthcoming.Google Scholar
- 6.Provost, F., Fawcett, T. (1997). Analysis and visualization of classifier performance: comparison under imprecise class and cost distributions. Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), Huntington Beach, CA.Google Scholar