About: OPTIMIZATION CHALLENGE: AREA UNDER RECEIVER OPERATING CHARACTERISTIC AS A LOSS FUNCTION IN GENERALIZED LINEAR MODELS     Goto   Sponge   NotDistinct   Permalink

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Description
  • The specific group of models are binary regression classifiers, e.g. the logit model. These models are built based on the quantitative loss functions like maximum likelihood estimation but their performance is obviously expressed by qualitative measure like accuracy or area under the receiver operating characteristic. In this paper we demonstrate the informational difference between quantitative a qualitative measures and suggest an alternative approach to setup of linear models based on the qualitative measure and genetic algorithms. We achieved statistically significant improvement of linear model setup by use of genetic algorithm, which overperforms equivalent logit model.
  • The specific group of models are binary regression classifiers, e.g. the logit model. These models are built based on the quantitative loss functions like maximum likelihood estimation but their performance is obviously expressed by qualitative measure like accuracy or area under the receiver operating characteristic. In this paper we demonstrate the informational difference between quantitative a qualitative measures and suggest an alternative approach to setup of linear models based on the qualitative measure and genetic algorithms. We achieved statistically significant improvement of linear model setup by use of genetic algorithm, which overperforms equivalent logit model. (en)
Title
  • OPTIMIZATION CHALLENGE: AREA UNDER RECEIVER OPERATING CHARACTERISTIC AS A LOSS FUNCTION IN GENERALIZED LINEAR MODELS
  • OPTIMIZATION CHALLENGE: AREA UNDER RECEIVER OPERATING CHARACTERISTIC AS A LOSS FUNCTION IN GENERALIZED LINEAR MODELS (en)
skos:prefLabel
  • OPTIMIZATION CHALLENGE: AREA UNDER RECEIVER OPERATING CHARACTERISTIC AS A LOSS FUNCTION IN GENERALIZED LINEAR MODELS
  • OPTIMIZATION CHALLENGE: AREA UNDER RECEIVER OPERATING CHARACTERISTIC AS A LOSS FUNCTION IN GENERALIZED LINEAR MODELS (en)
skos:notation
  • RIV/00216305:26220/10:PU88680!RIV11-GA0-26220___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA102/09/1897), Z(MSM0021630529)
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
  • 277339
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26220/10:PU88680
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • logit, linear model, regression classifier, genetic algorithm, area under receiver operating characteristic (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [2D048236E77A]
http://linked.open...v/mistoKonaniAkce
  • Brno University of Technology
http://linked.open...i/riv/mistoVydani
  • Brno, Czech Republic
http://linked.open...i/riv/nazevZdroje
  • MENDEL 2010
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
  • Kučera, Pavel
  • Honzík, Petr
  • Hynčica, Ondřej
  • Jirsík, Václav
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000288144100005
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Vysoké učení technické v Brně
https://schema.org/isbn
  • 978-80-214-4120-0
http://localhost/t...ganizacniJednotka
  • 26220
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