Comparison of Cost for Zero-One and Stage-Dependent Fuzzy Loss Function
In the paper we consider the two-stage binary classifier based on Bayes rule. Assuming that both the tree structure and the feature used at each non-terminal node have been specified, we present the expected total cost. This cost is considered for two types of loss function. First is the zero-one loss function and second is the node-dependent fuzzy loss function. The work focuses on the difference between the expected total costs for these two cases of loss function in the two-stage binary classifier. The obtained results are presented on the numerical example.
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- 6.Domingos, P.: MetaCost: A General Method for Making Classifiers Cost-Sensitive. In: Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining (KDD 1999), pp. 155–164 (1999)Google Scholar
- 10.Núñez, M.: The use of background knowledge in decision tree induction. Machine Learning 6(3), 231–250 (1991)Google Scholar
- 14.Tan, M.: Cost-sensitive learning of classification knowledge and its applications in robotics. Machine Learning 13, 7–33 (1993)Google Scholar
- 15.Turney, P.: Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm. Journal of Artificial Intelligence Research 2, 369–409 (1995)Google Scholar