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Description
  • Data mining je významným procesem v mhoha výzkumných a průmyslových odvětvích. Článek uvádí vyvíjející se struktur klasifikátorů, ve kterém jsou strmy generovány hybridní metodou, která spojuje metaheuristiku mravenčích kolonií a evolučního algoritmu. Výhodou metody je společné použití stochastického a populačního příestupu, který dovoluje rychlejší evoluci než v případě samostatného použití jednotlivých metod. Metoda se podobá metodě generování náhodného lesu, a tak je možné ji použití pro selekci příznaků. Článek také uvádí odhad parametů metody. Uvádíme přehled testů nad databází UCI a biomedicínskými daty. V případě datbáze MIT-BIH bylo dosaženo senzitivity 93.22 % a specificity 87.13 %. (cs)
  • In many industry and research areas, data mining is a crucial process. This paper presents an evolving structure of classifiers (random forest) where the trees are generated by hybrid method combining Ant Colony metaheuristics and Evolutionary computing technique. The method benefits from the stochastic process and population approach, which allows the algorithm to evolve more efficiently than each method alone. As the method is similar to random forest generation, it can be also used for feature selection. The paper also discusses the parameter estimation for the method. Tests on real data (UCI and real biomedical data) have been performed and evaluated. The average accuracy of the method over MIT-BIH database with normalized data and equalized classes is sensitivity 93.22 % and specificity 87.13 %.
  • In many industry and research areas, data mining is a crucial process. This paper presents an evolving structure of classifiers (random forest) where the trees are generated by hybrid method combining Ant Colony metaheuristics and Evolutionary computing technique. The method benefits from the stochastic process and population approach, which allows the algorithm to evolve more efficiently than each method alone. As the method is similar to random forest generation, it can be also used for feature selection. The paper also discusses the parameter estimation for the method. Tests on real data (UCI and real biomedical data) have been performed and evaluated. The average accuracy of the method over MIT-BIH database with normalized data and equalized classes is sensitivity 93.22 % and specificity 87.13 %. (en)
Title
  • Hybridized Swarm Metaheuristics for Evolutionary Random Forest Generation
  • Hybridní metaheuristika pro evoluční vytváření náhodného lesu (cs)
  • Hybridized Swarm Metaheuristics for Evolutionary Random Forest Generation (en)
skos:prefLabel
  • Hybridized Swarm Metaheuristics for Evolutionary Random Forest Generation
  • Hybridní metaheuristika pro evoluční vytváření náhodného lesu (cs)
  • Hybridized Swarm Metaheuristics for Evolutionary Random Forest Generation (en)
skos:notation
  • RIV/68407700:21230/07:03132666!RIV08-MSM-21230___
http://linked.open.../vavai/riv/strany
  • 150;155
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM6840770012)
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
  • 425040
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/07:03132666
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Ant Colony Optimization; Artificial Inteligence; Clustering; Nature Inspired Methods; Particle Swarm (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [1B0B44BF8945]
http://linked.open...v/mistoKonaniAkce
  • Kaiserslautern
http://linked.open...i/riv/mistoVydani
  • Piscataway
http://linked.open...i/riv/nazevZdroje
  • Seventh International Conference on Hybrid Intelligent Systems
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Burša, Miroslav
  • Lhotská, Lenka
  • Macaš, Martin
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
https://schema.org/isbn
  • 978-0-7695-2946-2
http://localhost/t...ganizacniJednotka
  • 21230
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