Abstract
Previous work considering both test and misclassification costs rely on the assumption that the test cost and the misclassification cost must be defined on the same cost scale. However, it can be difficult to define the multiple costs on the same cost scale. In our previous work, a novel yet efficient approach for involving multiple cost scales is proposed. Specifically speaking, we first introduce a new test-sensitive decision tree with two kinds of cost scales, that minimizes the one kind of cost and control the other in a given specific budget. In this paper, a dynamic test strategy with known information utilization and global resource control is proposed to keep the minimization of overall target cost. Our work will be useful in many urgent diagnostic tasks involving target cost minimization and resource consumption for obtaining missing information.
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References
Ling, C., Yang, Q., Wang, J., Zhang, S.: Decision trees with minimal costs. In: Proceedings of 2004 International Conference on Machine Learning (2004)
Turney, P.D.: Types of cost in inductive concept learning. In: Workshop on Cost-Sensitive Learning at the Seventeenth International Conference on Machine Learning, Stanford University, California (2000)
UCI Repository of machine learning databases. Department of Information and Computer Science. University of California, Irvine, http://www.ics.uci.edu/~mlearn/MLRepository.html
Turney, P.D.: Cost-sensitive classication: Empirical evaluation of a hybrid genetic decision tree induction algorithm. Journal of Articial Intelligence Research 2, 369–409 (1995)
Machine Learning. McGraw Hills, New York
Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp. 1022–1027. Morgan Kaufmann, San Francisco
Greiner, R., Grove, A.J., Roth, D.: Learning cost-sensitive active classiers. Articial Intelligence 139(2), 137–174 (2002)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Qin, Z., Zhang, C., Zhang, S.: Cost-sensitive Decision Trees With Multiple Cost Scales. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 380–390. Springer, Heidelberg (2004)
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Qin, Z., Zhang, C., Xie, X., Zhang, S. (2005). Dynamic Test-Sensitive Decision Trees with Multiple Cost Scales. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539506_52
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DOI: https://doi.org/10.1007/11539506_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28312-6
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