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Statements

Subject Item
n2:RIV%2F68407700%3A21240%2F10%3A00166078%21RIV11-MSM-21240___
rdf:type
n4:Vysledek skos:Concept
dcterms:description
Most of Feature Ranking and Feature Selection approaches can be used for categorial data only. Some of them rely on statistical measures of the data, some are tailored to a specific data mining algorithm (wrapper approach). In this paper we present new methods for feature ranking and selection obtained as a combination of the above mentioned approaches. The data mining algorithm (GAME) is designed for numerical data, but it can be applied to categorial data as well. It incorporates feature selection mechanisms and new methods, proposed in this paper, derive feature ranking from final data mining model. The rank of each feature selected by model is computed by processing correlations of outputs between neighboring model?s neurons in different ways. We used four different methods based on fuzzy logic, certainty factors and simple calculus. The performance of these four feature ranking methods was tested on artificial data sets, on well known Ionosphere data set and on well known Housing Most of Feature Ranking and Feature Selection approaches can be used for categorial data only. Some of them rely on statistical measures of the data, some are tailored to a specific data mining algorithm (wrapper approach). In this paper we present new methods for feature ranking and selection obtained as a combination of the above mentioned approaches. The data mining algorithm (GAME) is designed for numerical data, but it can be applied to categorial data as well. It incorporates feature selection mechanisms and new methods, proposed in this paper, derive feature ranking from final data mining model. The rank of each feature selected by model is computed by processing correlations of outputs between neighboring model?s neurons in different ways. We used four different methods based on fuzzy logic, certainty factors and simple calculus. The performance of these four feature ranking methods was tested on artificial data sets, on well known Ionosphere data set and on well known Housing
dcterms:title
New Methods for Feature Ranking New Methods for Feature Ranking
skos:prefLabel
New Methods for Feature Ranking New Methods for Feature Ranking
skos:notation
RIV/68407700:21240/10:00166078!RIV11-MSM-21240___
n3:aktivita
n21:V n21:Z
n3:aktivity
V, Z(MSM6840770012)
n3:dodaniDat
n5:2011
n3:domaciTvurceVysledku
n14:1266500
n3:druhVysledku
n10:D
n3:duvernostUdaju
n18:S
n3:entitaPredkladatele
n11:predkladatel
n3:idSjednocenehoVysledku
274719
n3:idVysledku
RIV/68407700:21240/10:00166078
n3:jazykVysledku
n6:eng
n3:klicovaSlova
Feature Ranking; Feature Selection; Correlation; Fuzzy Logic; Certainty Factor
n3:klicoveSlovo
n7:Correlation n7:Certainty%20Factor n7:Fuzzy%20Logic n7:Feature%20Selection n7:Feature%20Ranking
n3:kontrolniKodProRIV
[93F0CFA2E262]
n3:mistoKonaniAkce
Praha
n3:mistoVydani
Praha
n3:nazevZdroje
Workshop 2010
n3:obor
n15:IN
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
3
n3:rokUplatneniVysledku
n5:2010
n3:tvurceVysledku
Kordík, Pavel Šnorek, Miroslav Pilný, Aleš
n3:typAkce
n17:EUR
n3:zahajeniAkce
2010-02-22+01:00
n3:zamer
n13:MSM6840770012
s:numberOfPages
2
n12:hasPublisher
České vysoké učení technické v Praze
n19:isbn
978-80-01-04513-8
n20:organizacniJednotka
21240