Test-cost sensitive classification based on conditioned loss functions

Date
2007-09
Advisor
Instructor
Source Title
ECML '07 Proceedings of the 18th European conference on Machine Learning
Print ISSN
Electronic ISSN
Publisher
Springer
Volume
Issue
Pages
551 - 558
Language
English
Type
Conference Paper
Journal Title
Journal ISSN
Volume Title
Abstract

We report a novel approach for designing test-cost sensitive classifiers that consider the misclassification cost together with the cost of feature extraction utilizing the consistency behavior for the first time. In this approach, we propose to use a new Bayesian decision theoretical framework in which the loss is conditioned with the current decision and the expected decisions after additional features are extracted as well as the consistency among the current and expected decisions. This approach allows us to force the feature extraction for samples for which the current and expected decisions are inconsistent. On the other hand, it forces not to extract any features in the case of consistency, leading to less costly but equally accurate decisions. In this work, we apply this approach to a medical diagnosis problem and demonstrate that it reduces the overall feature extraction cost up to 47.61 percent without decreasing the accuracy. © Springer-Verlag Berlin Heidelberg 2007.

Course
Other identifiers
Book Title
Keywords
Bayesian networks, Decision theory, Feature extraction, Problem solving, Sensitivity analysis, Bayesian decision theoretical framework, Conditioned loss functions, Test-cost sensitive classification, Classification (of information)
Citation
Published Version (Please cite this version)