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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F08%3A03145492%21RIV09-MSM-21230___
rdf:type
n3:Vysledek skos:Concept
dcterms: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. 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.
dcterms:title
Feature Ranking Derived from Data Mining Process Feature Ranking Derived from Data Mining Process Feature Ranking Derived from Data Mining Process
skos:prefLabel
Feature Ranking Derived from Data Mining Process Feature Ranking Derived from Data Mining Process Feature Ranking Derived from Data Mining Process
skos:notation
RIV/68407700:21230/08:03145492!RIV09-MSM-21230___
n4:aktivita
n9:P n9:Z
n4:aktivity
P(KJB201210701), Z(MSM6840770012)
n4:dodaniDat
n11:2009
n4:domaciTvurceVysledku
n7:1266500 n7:7035586 n7:2874695
n4:druhVysledku
n22:D
n4:duvernostUdaju
n14:S
n4:entitaPredkladatele
n15:predkladatel
n4:idSjednocenehoVysledku
367780
n4:idVysledku
RIV/68407700:21230/08:03145492
n4:jazykVysledku
n19:eng
n4:klicovaSlova
Feature Ranking, FAKE-GAME, Niching Genetic Algorithm, Median, Artificial Neural Network
n4:klicoveSlovo
n8:Feature%20Ranking n8:Median n8:Niching%20Genetic%20Algorithm n8:FAKE-GAME n8:Artificial%20Neural%20Network
n4:kontrolniKodProRIV
[9593DB06DBA6]
n4:mistoKonaniAkce
Prague
n4:mistoVydani
Heidelberg
n4:nazevZdroje
Artificial Neural Networks - ICANN 2008
n4:obor
n21:IN
n4:pocetDomacichTvurcuVysledku
3
n4:pocetTvurcuVysledku
3
n4:projekt
n18:KJB201210701
n4:rokUplatneniVysledku
n11:2008
n4:tvurceVysledku
Šnorek, Miroslav Pilný, Aleš Kordík, Pavel
n4:typAkce
n13:EUR
n4:wos
000259567200092
n4:zahajeniAkce
2008-09-03+02:00
n4:zamer
n17:MSM6840770012
s:issn
0302-9743
s:numberOfPages
10
n20:hasPublisher
Springer-Verlag
n12:isbn
978-3-540-87558-1
n16:organizacniJednotka
21230