About: New Feature Selection Method for Multi-class Data: Iteratively Weighted AUC (IWA)     Goto   Sponge   Distinct   Permalink

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Description
  • This paper deals with the new filter feature selection method Iteratively Weighted Area under Receiver Operating Characteristic (IWA). It is aimed for the multi-class problems with quantitative inputs. The experiments prove its superior quality in comparison to the equivalent methods.
  • This paper deals with the new filter feature selection method Iteratively Weighted Area under Receiver Operating Characteristic (IWA). It is aimed for the multi-class problems with quantitative inputs. The experiments prove its superior quality in comparison to the equivalent methods. (en)
Title
  • New Feature Selection Method for Multi-class Data: Iteratively Weighted AUC (IWA)
  • New Feature Selection Method for Multi-class Data: Iteratively Weighted AUC (IWA) (en)
skos:prefLabel
  • New Feature Selection Method for Multi-class Data: Iteratively Weighted AUC (IWA)
  • New Feature Selection Method for Multi-class Data: Iteratively Weighted AUC (IWA) (en)
skos:notation
  • RIV/00216305:26220/11:PU95783!RIV12-MSM-26220___
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http://linked.open...avai/riv/aktivita
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  • P(1M0567), P(GA102/09/1897)
http://linked.open...vai/riv/dodaniDat
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  • 215795
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  • RIV/00216305:26220/11:PU95783
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  • feature selection, classification, multi-class, ROC, AUC, IWA (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [ACF0DE77E8C4]
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  • Praha
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  • Neuveden
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  • The 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications
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  • Kučera, Pavel
  • Honzík, Petr
  • Haupt, Daniel
  • Hynčica, Ondřej
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number of pages
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  • Neuveden
https://schema.org/isbn
  • 978-1-4577-1423-8
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  • 26220
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