Qualitative test-cost sensitive classification
Author
Cebe, M.
Gunduz Demir, C.
Date
2010Source Title
Pattern Recognition Letters
Print ISSN
0167-8655
Publisher
Elsevier BV
Volume
31
Issue
13
Pages
2043 - 2051
Language
English
Type
ArticleItem Usage Stats
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Abstract
This paper reports a new framework for test-cost sensitive classification. It introduces a new loss function definition, in which misclassification cost and cost of feature extraction are combined qualitatively and the loss is conditioned with current and estimated decisions as well as their consistency. This loss function definition is motivated with the following issues. First, for many applications, the relation between different types of costs can be expressed roughly and usually only in terms of ordinal relations, but not as a precise quantitative number. Second, the redundancy between features can be used to decrease the cost; it is possible not to consider a new feature if it is consistent with the existing ones. In this paper, we show the feasibility of the proposed framework for medical diagnosis problems. Our experiments demonstrate that this framework is efficient to significantly decrease feature extraction cost without decreasing accuracy. © 2010 Elsevier B.V. All rights reserved.
Keywords
Cost - sensitive learningFeature extraction cost
Feature selection
Qualitative decision theory
Cost of feature extraction
Cost sensitive classifications
Loss functions
Medical diagnosis
Misclassification costs
Qualitative test
Costs
Decision theory
Diagnosis
Medical problems
Feature extraction