Costs-Sensitive Classification in Multistage Classifier with Fuzzy Observations of Object Features
In the paper the problem of cost in hierarchical classifier is presented. Assuming that both the tree structure and the feature used at each non-terminal node have been specified, we present the expected total cost for two cases. The first one concerns the non fuzzy observation of object features, the second concerns the fuzzy observation. At the end of the work the difference between expected total cost of fuzzy and non fuzzy data is determined. Obtained results relate to the locally optimal strategy of Bayes multistage classifier.
Unable to display preview. Download preview PDF.
- 1.Núñez, M.: The use of background knowledge in decision tree induction. Machine Learning 6(3), 231–250 (1991)Google Scholar
- 4.Tan, M.: Cost-sensitive learning of classification knowledge and its applications in robotics. Machine Learning 13, 7–33 (1993)Google Scholar
- 5.Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and regression trees, California, Wadsworth (1984)Google Scholar
- 6.Knoll, U., Nakhaeizadeh, G., Tausend, B.: Cost-sensitive pruning of decision trees. In: Proceedings of the Eight European Conference on Machine Learning ECML, vol. 94, pp. 383–386 (1994)Google Scholar
- 9.Turney, P.: Cost-sensitive classificcation: Empirical evaluation of a hybrid genetic decision tree induction algorithm. Journal of Artificial Intelligence Research 2, 369–409 (1995)Google Scholar