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Description
  • In this paper we compare and evaluate the use of the following methods: Ant Colony inspired Clustering, Ant Colony inspired method for Decision Tree generation, Radial Basis Function Neural Networks with different learning algorithms and compare them to classical approaches, such as hierarchical clustering and k-means. We have evaluated the methods on the annotated MIT-BIH database. The best-performing has been the agglomerative hierarchical clustering (Se=94.3, Sp=74.1), however it is practically unusable as it is memory and computational demanding. Acceptable results (complexity vs. error) have been obtained by the Ant-Colony inspired method for Decision tree generation (Se=93.1, Sp=72.8).
  • In this paper we compare and evaluate the use of the following methods: Ant Colony inspired Clustering, Ant Colony inspired method for Decision Tree generation, Radial Basis Function Neural Networks with different learning algorithms and compare them to classical approaches, such as hierarchical clustering and k-means. We have evaluated the methods on the annotated MIT-BIH database. The best-performing has been the agglomerative hierarchical clustering (Se=94.3, Sp=74.1), however it is practically unusable as it is memory and computational demanding. Acceptable results (complexity vs. error) have been obtained by the Ant-Colony inspired method for Decision tree generation (Se=93.1, Sp=72.8). (en)
  • Článek uvádí srovnání a vyhodnocení následujících metod: shlukování inspirované koloniemi mravenců, metoda pro vytváření rozhodovacích stromů inspirovaná koloniemi mravenců, neuronové sítě typu RBF s využitím různých metod učení. Tyto metody jsou porovnány s klasickými přístupy, tj. hierarchické shlukování a k-means. Vyhodnocení je provedeno s použitím databáze MIT-BIH. Nejlepších výsledků (Se=94.3, Sp=74.1) dosáhlo hierarchické shlukování, které je však velmi náročné. Vhodným kompromisem je (komplexita vs. chyba) je metoda vytváření rozhodovacích stromů inspirovaná chováním mravenčích kolonií (Se=93.1, Sp=72.8). (cs)
Title
  • Nature Inspired Concepts in the Electrocardiogram Interpretation Process
  • Použití přírodou inspirovaných konceptů v procesu interpretace elektrokardiogramu (cs)
  • Nature Inspired Concepts in the Electrocardiogram Interpretation Process (en)
skos:prefLabel
  • Nature Inspired Concepts in the Electrocardiogram Interpretation Process
  • Použití přírodou inspirovaných konceptů v procesu interpretace elektrokardiogramu (cs)
  • Nature Inspired Concepts in the Electrocardiogram Interpretation Process (en)
skos:notation
  • RIV/68407700:21230/08:03151586!RIV09-MSM-21230___
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
  • 381812
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/08:03151586
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • ACO_DTree; ant colony optimization; electrocardiogram interpretation; electrocardiogram processing (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [CB86E5487650]
http://linked.open...v/mistoKonaniAkce
  • Bologna
http://linked.open...i/riv/mistoVydani
  • Missouri
http://linked.open...i/riv/nazevZdroje
  • Computers in Cardiology 2008, Volume 35
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
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
issn
  • 0276-6574
number of pages
http://purl.org/ne...btex#hasPublisher
  • Computers in Cardiology
http://localhost/t...ganizacniJednotka
  • 21230
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