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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F09%3A00326643%21RIV10-MSM-67985556
rdf:type
n4:Vysledek skos:Concept
dcterms:description
In feature selection the effect of over-fitting may lead to serious degradation of generalization ability. We introduce the concept of combining multiple feature selection criteria in feature selection methods with the aim to obtain feature subsets that generalize better. The concept is applicable with many existing feature selection methods. Here we discuss in more detail the family of sequential search methods. The concept does not specify which criteria to combine – to illustrate its feasibility we give a simple example of combining the estimated accuracy of k-nearest neighbor classifiers for various k.We perform the experiments on a number of datasets. The potential to improve is clearly seen on improved classifier performance on independent test data as well as on improved feature selection stability. In feature selection the effect of over-fitting may lead to serious degradation of generalization ability. We introduce the concept of combining multiple feature selection criteria in feature selection methods with the aim to obtain feature subsets that generalize better. The concept is applicable with many existing feature selection methods. Here we discuss in more detail the family of sequential search methods. The concept does not specify which criteria to combine – to illustrate its feasibility we give a simple example of combining the estimated accuracy of k-nearest neighbor classifiers for various k.We perform the experiments on a number of datasets. The potential to improve is clearly seen on improved classifier performance on independent test data as well as on improved feature selection stability.
dcterms:title
Criteria Ensembles in Feature Selection Criteria Ensembles in Feature Selection
skos:prefLabel
Criteria Ensembles in Feature Selection Criteria Ensembles in Feature Selection
skos:notation
RIV/67985556:_____/09:00326643!RIV10-MSM-67985556
n5:aktivita
n8:P n8:Z
n5:aktivity
P(1M0572), P(2C06019), P(GA102/08/0593), Z(AV0Z10750506)
n5:dodaniDat
n18:2010
n5:domaciTvurceVysledku
n14:6617972 n14:5728525 n14:4788575
n5:druhVysledku
n21:D
n5:duvernostUdaju
n13:S
n5:entitaPredkladatele
n16:predkladatel
n5:idSjednocenehoVysledku
308547
n5:idVysledku
RIV/67985556:_____/09:00326643
n5:jazykVysledku
n20:eng
n5:klicovaSlova
feature selection; criterion; ensemble; combining criteria
n5:klicoveSlovo
n7:criterion n7:feature%20selection n7:combining%20criteria n7:ensemble
n5:kontrolniKodProRIV
[B24DD5157524]
n5:mistoKonaniAkce
Reykjavik
n5:mistoVydani
Berlin Heidelberg
n5:nazevZdroje
Multiple Classifier Systems, LNCS 5519
n5:obor
n10:BD
n5:pocetDomacichTvurcuVysledku
3
n5:pocetTvurcuVysledku
3
n5:projekt
n12:1M0572 n12:2C06019 n12:GA102%2F08%2F0593
n5:rokUplatneniVysledku
n18:2009
n5:tvurceVysledku
Somol, Petr Pudil, Pavel Grim, Jiří
n5:typAkce
n6:WRD
n5:zahajeniAkce
2009-06-10+02:00
n5:zamer
n19:AV0Z10750506
s:numberOfPages
10
n11:hasPublisher
Springer-Verlag
n17:isbn
3-642-02325-8