About: The Problem of Fragile Feature Subset Preference in Feature Selection Methods and A Proposal of Algorithmic Workaround     Goto   Sponge   NotDistinct   Permalink

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Description
  • We point out a problem inherent in the optimization scheme of many popular feature selection methods. It follows from the implicit assumption that higher feature selection criterion value always indicates more preferable subset even if the value difference is marginal. This assumption ignores the reliability issues of particular feature preferences, overfitting and feature acquisition cost. We propose an algorithmic extension applicable to many standard feature selection methods allowing better control over feature subset preference. We show experimentally that the proposed mechanism is capable of reducing the size of selected subsets as well as improving classifier generalization.
  • We point out a problem inherent in the optimization scheme of many popular feature selection methods. It follows from the implicit assumption that higher feature selection criterion value always indicates more preferable subset even if the value difference is marginal. This assumption ignores the reliability issues of particular feature preferences, overfitting and feature acquisition cost. We propose an algorithmic extension applicable to many standard feature selection methods allowing better control over feature subset preference. We show experimentally that the proposed mechanism is capable of reducing the size of selected subsets as well as improving classifier generalization. (en)
Title
  • The Problem of Fragile Feature Subset Preference in Feature Selection Methods and A Proposal of Algorithmic Workaround
  • The Problem of Fragile Feature Subset Preference in Feature Selection Methods and A Proposal of Algorithmic Workaround (en)
skos:prefLabel
  • The Problem of Fragile Feature Subset Preference in Feature Selection Methods and A Proposal of Algorithmic Workaround
  • The Problem of Fragile Feature Subset Preference in Feature Selection Methods and A Proposal of Algorithmic Workaround (en)
skos:notation
  • RIV/67985556:_____/10:00348710!RIV11-MSM-67985556
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1M0572), P(2C06019), P(GA102/07/1594), P(GA102/08/0593), Z(AV0Z10750506)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 282138
http://linked.open...ai/riv/idVysledku
  • RIV/67985556:_____/10:00348710
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • feature selection; machine learning; over-fitting; classification; feature weights; weighted features; feature acquisition cost (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [F3D86A04B48F]
http://linked.open...v/mistoKonaniAkce
  • Istanbul
http://linked.open...i/riv/mistoVydani
  • Istanbul
http://linked.open...i/riv/nazevZdroje
  • Proc. 2010 Int. Conf. on Pattern Recognition
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Grim, Jiří
  • Pudil, Pavel
  • Somol, Petr
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • IEEE Computer Society
https://schema.org/isbn
  • 978-0-7695-4109-9
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