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      Qualitative test-cost sensitive classification

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      Author
      Cebe, M.
      Gunduz Demir, C.
      Date
      2010
      Source Title
      Pattern Recognition Letters
      Print ISSN
      0167-8655
      Publisher
      Elsevier BV
      Volume
      31
      Issue
      13
      Pages
      2043 - 2051
      Language
      English
      Type
      Article
      Item 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 learning
      Feature 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
      Permalink
      http://hdl.handle.net/11693/22197
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/j.patrec.2010.05.028
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