Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/94192
Authors: 
Rybizki, Lydia
Year of Publication: 
2014
Series/Report no.: 
IWQW Discussion Paper Series 01/2014
Abstract: 
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.
Subjects: 
unequal misclassification costs
proper scoring rules
AUC
boosting
CART
model selection
pruning
early stopping
Document Type: 
Working Paper

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