Abstract
In this paper, we present an evaluation of a rule evaluation support method for post-processing of mined results with rule evaluation models based on objective indices. To reduce the costs of rule evaluation task, which is one of the key procedures in data mining post-processing, we have developed the rule evaluation support method with rule evaluation models, which are obtained with objective indices of mined classification rules and evaluations of a human expert for each rule. Then we have evaluated performances of learning algorithms for constructing rule evaluation models on the meningitis data mining as an actual problem and five rulesets from the five kinds of UCI datasets. With these results, we show the availability of our rule evaluation support method.
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References
Hilderman, R.J., Hamilton, H.J.: Knowledge Discovery and Measure of Interest. Kluwer Academic Publishers, Dordrecht (2001)
Tan, P.N., Kumar, V., Srivastava, J.: Selecting the Right Interestingness Measure for Association Patterns. In: Proc. of Int. Conf. on Knowledge Discovery and Data Mining KDD-2002, pp. 32–41 (2002)
Yao, Y.Y., Zhong, N.: An Analysis of Quantitative Measures Associated with Rules. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 479–488. Springer, Heidelberg (1999)
Ohsaki, M., Kitaguchi, S., Kume, S., Yokoi, H., Yamaguchi, T.: Evaluation of Rule Interestingness Measures with a Clinical Dataset on Hepatitis. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 362–373. Springer, Heidelberg (2004)
Hatazawa, H., Negishi, N., Suyama, A., Tsumoto, S., Yamaguchi, T.: Knowledge Discovery Support from a Meningoencephalitis Database Using an Automatic Composition Tool for Inductive Applications. In: Proc. of KDD Challenge 2000 in conjunction with PAKDD 2000, pp. 28–33 (2000)
Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. University of California, Department of Information and Computer Science, Irvine, CA (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Witten, I.H., Frank, E.: DataMining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2000)
Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Hinton, G.E.: Learning distributed representations of concepts. In: Proc. of 8th Annual Conference of the Cognitive Science Society, Amherest, MA (1986) (reprinted in Morris, R.G.M. (ed.))
Platt, J.: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning, pp. 185–208. MIT Press, Cambridge (1999)
Frank, E., Wang, Y., Inglis, S., Holmes, G., Witten, I.H.: Using model trees for classification. Machine Learning 32(1), 63–76 (1998)
Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Machine Learning 11, 63–91 (1993)
Gago, P., Bento, C.: A Metric for Selection of the Most Promising Rules. In: Proc. of Euro. Conf. on the Principles of Data Mining and Knowledge Discovery PKDD-1998, pp. 19–27 (1998)
Zhong, N., Yao, Y.Y., Ohshima, M.: Peculiarity Oriented Multi-Database Mining. IEEE Trans. on Knowledge and Data Engineering 15(4), 952–960 (2003)
Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: Proc. of the Fifteenth International Conference on Machine Learning, pp. 144–151 (1998)
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Abe, H., Tsumoto, S., Ohsaki, M., Yamaguchi, T. (2006). Developing a Rule Evaluation Support Method Based on Objective Indices. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_66
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DOI: https://doi.org/10.1007/11795131_66
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