Arbeitspapier
Learning cost sensitive binary classification rules accounting for uncertain and unequal misclassification costs
This paper proposes cost sensitive criteria for constructing classification rules by supervised learning methods. Reinterpreting established loss functions and considering those introduced by Buja, Stuetzle, et al. (2005) and Hand (2009), we identify criteria reflecting different degrees of information about misclassification costs. To adapt classification methodology to practical cost considerations, we suggest the use of these criteria for different model selection approaches in supervised learning. In addition, we investigate the effects of cost sensitive adaptations in CART and boosting and conclude that adaptations are more promising in the selection rather than in the estimation step.
- Language
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Englisch
- Bibliographic citation
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Series: IWQW Discussion Papers ; No. 01/2014
- Classification
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Wirtschaft
- Subject
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unequal misclassification costs
proper scoring rules
AUC
boosting
CART
model selection
pruning
early stopping
- Event
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Geistige Schöpfung
- (who)
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Rybizki, Lydia
- Event
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Veröffentlichung
- (who)
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Friedrich-Alexander-Universität Erlangen-Nürnberg, Institut für Wirtschaftspolitik und Quantitative Wirtschaftsforschung (IWQW)
- (where)
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Nürnberg
- (when)
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2014
- Handle
- Last update
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10.03.2025, 11:41 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Rybizki, Lydia
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Institut für Wirtschaftspolitik und Quantitative Wirtschaftsforschung (IWQW)
Time of origin
- 2014
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