About: Feature Ranking Derived from Data Mining Process

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	Most common feature ranking methods are based on the statistical approach. This paper compare several statistical methods with new method for feature ranking derived from data mining process. This method ranks features depending on percentage of child units that survived the selection process. A child unit is a processing element transforming the parent input features to the output. After training, units are interconnected in the feedforward hybrid neural network called GAME. The selection process is realized by means of niching genetic algorithm, where units connected to least significant features starve and fade from population. Parameters of new FR algorithm are investigated and comparison among different methods is presented on well known real world and artificial data sets. Most common feature ranking methods are based on the statistical approach. This paper compare several statistical methods with new method for feature ranking derived from data mining process. This method ranks features depending on percentage of child units that survived the selection process. A child unit is a processing element transforming the parent input features to the output. After training, units are interconnected in the feedforward hybrid neural network called GAME. The selection process is realized by means of niching genetic algorithm, where units connected to least significant features starve and fade from population. Parameters of new FR algorithm are investigated and comparison among different methods is presented on well known real world and artificial data sets. (en) Most common feature ranking methods are based on the statistical approach. This paper compare several statistical methods with new method for feature ranking derived from data mining process. This method ranks features depending on percentage of child units that survived the selection process. A child unit is a processing element transforming the parent input features to the output. After training, units are interconnected in the feedforward hybrid neural network called GAME. The selection process is realized by means of niching genetic algorithm, where units connected to least significant features starve and fade from population. Parameters of new FR algorithm are investigated and comparison among different methods is presented on well known real world and artificial data sets. (cs)
Title	Feature Ranking Derived from Data Mining Process Feature Ranking Derived from Data Mining Process (en) Feature Ranking Derived from Data Mining Process (cs)
skos:prefLabel	Feature Ranking Derived from Data Mining Process Feature Ranking Derived from Data Mining Process (en) Feature Ranking Derived from Data Mining Process (cs)
skos:notation	RIV/68407700:21230/08:03145492!RIV09-MSM-21230___
http://linked.open...avai/riv/aktivita	P Z
http://linked.open...avai/riv/aktivity	P(KJB201210701), Z(MSM6840770012)
http://linked.open...vai/riv/dodaniDat	2009
http://linked.open...aciTvurceVysledku	Pilný, Aleš Kordík, Pavel Šnorek, Miroslav
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	367780
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/08:03145492
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Feature Ranking, FAKE-GAME, Niching Genetic Algorithm, Median, Artificial Neural Network (en)
http://linked.open.../riv/klicoveSlovo	Artificial Neural Network Median FAKE-GAME Niching Genetic Algorithm Feature Ranking
http://linked.open...ontrolniKodProRIV	[9593DB06DBA6]
http://linked.open...v/mistoKonaniAkce	Prague
http://linked.open...i/riv/mistoVydani	Heidelberg
http://linked.open...i/riv/nazevZdroje	Artificial Neural Networks - ICANN 2008
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	3 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...vavai/riv/projekt	Automated Knowledge Extraction
http://linked.open...UplatneniVysledku	2008
http://linked.open...iv/tvurceVysledku	Pilný, Aleš Kordík, Pavel Šnorek, Miroslav
http://linked.open...vavai/riv/typAkce	EUR - Evropská
http://linked.open...ain/vavai/riv/wos	000259567200092
http://linked.open.../riv/zahajeniAkce	2008-09-03 (xsd:date)
http://linked.open...n/vavai/riv/zamer	Transdisciplinární výzkum v oblasti biomedicínského inženýrství II
issn	0302-9743
number of pages	10 (xsd:int)
http://purl.org/ne...btex#hasPublisher	Springer-Verlag
https://schema.org/isbn	978-3-540-87558-1
http://localhost/t...ganizacniJednotka	21230

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