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Statements

Subject Item
n2:RIV%2F68407700%3A21240%2F09%3A00159293%21RIV14-MSM-21240___
rdf:type
n9:Vysledek skos:Concept
rdfs:seeAlso
http://iwim2009.felk.cvut.cz/files/ProceedingsIWIM09.pdf
dcterms:description
Most of Feature Ranking and Feature Selection approaches can be used for categorial data only. 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 and on well known real world data sets. These methods produce ranking consistent with recently published studies. Most of Feature Ranking and Feature Selection approaches can be used for categorial data only. 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 and on well known real world data sets. These methods produce ranking consistent with recently published studies.
dcterms:title
Correlation-based Feature Ranking in Combination with Embedded Feature Selection Correlation-based Feature Ranking in Combination with Embedded Feature Selection
skos:prefLabel
Correlation-based Feature Ranking in Combination with Embedded Feature Selection Correlation-based Feature Ranking in Combination with Embedded Feature Selection
skos:notation
RIV/68407700:21240/09:00159293!RIV14-MSM-21240___
n4:aktivita
n8:Z n8:P
n4:aktivity
P(KJB201210701), Z(MSM6840770012)
n4:dodaniDat
n15:2014
n4:domaciTvurceVysledku
n12:1266500
n4:druhVysledku
n13:O
n4:duvernostUdaju
n11:S
n4:entitaPredkladatele
n6:predkladatel
n4:idSjednocenehoVysledku
308410
n4:idVysledku
RIV/68407700:21240/09:00159293
n4:jazykVysledku
n18:eng
n4:klicovaSlova
Feature Ranking; Feature Selection; Correlation; FAKE-GAME; Embedded Model.
n4:klicoveSlovo
n5:Correlation n5:Feature%20Ranking n5:Feature%20Selection n5:Embedded%20Model. n5:FAKE-GAME
n4:kontrolniKodProRIV
[7DD00C185708]
n4:obor
n14:IN
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
4
n4:projekt
n16:KJB201210701
n4:rokUplatneniVysledku
n15:2009
n4:tvurceVysledku
Pilný, Aleš Kordík, Pavel Oertel, W. Šnorek, Miroslav
n4:zamer
n17:MSM6840770012
n10:organizacniJednotka
21240