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      Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals

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      Author(s)
      Güvenir, H. A.
      Demiröz, G.
      İlter, N.
      Date
      1998
      Source Title
      Artificial Intelligence in Medicine
      Print ISSN
      0933-3657
      Electronic ISSN
      1873-2860
      Publisher
      Elsevier
      Volume
      13
      Issue
      3
      Pages
      147 - 165
      Language
      English
      Type
      Article
      Item Usage Stats
      138
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      Abstract
      A new classification algorithm, called VFI5 (for Voting Feature Intervals), is developed and applied to problem of differential diagnosis of erythemato-squamous diseases. The domain contains records of patients with known diagnosis. Given a training set of such records, the VFI5 classifier learns how to differentiate a new case in the domain. VFI5 represents a concept in the form of feature intervals on each feature dimension separately. classification in the VFI5 algorithm is based on a real-valued voting. Each feature equally participates in the voting process and the class that receives the maximum amount of votes is declared to be the predicted class. The performance of the VFI5 classifier is evaluated empirically in terms of classification accuracy and running time. (C) 1998 Elsevier Science B.V. All rights reserved.
      Keywords
      Machine Learning
      Differential Diagnosis
      Erythemato-Squamous
      Voting Feature Intervals
      Permalink
      http://hdl.handle.net/11693/11003
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/S0933-3657(98)00028-1
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      • Department of Computer Engineering 1435
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