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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F11%3A00368741%21RIV12-AV0-67985556
rdf:type
n12:Vysledek skos:Concept
dcterms:description
The purpose of feature selection in machine learning is at least two-fold – saving measurement acquisition costs and reducing the negative effects of the curse of dimensionality with the aim to improve the accuracy of the models and the classification rate of classifiers with respect to previously unknown data. Yet it has been shown recently that the process of feature selection itself can be negatively affected by the very same curse of dimensionality – feature selection methods may easily over-fit or perform unstably. Such an outcome is unlikely to generalize well and the resulting recognition system may fail to deliver the expectable performance. In many tasks, it is therefore crucial to employ additional mechanisms of making the feature selection process more stable and resistant the curse of dimensionality effects. In this paper we discuss three different approaches to reducing this problem. The purpose of feature selection in machine learning is at least two-fold – saving measurement acquisition costs and reducing the negative effects of the curse of dimensionality with the aim to improve the accuracy of the models and the classification rate of classifiers with respect to previously unknown data. Yet it has been shown recently that the process of feature selection itself can be negatively affected by the very same curse of dimensionality – feature selection methods may easily over-fit or perform unstably. Such an outcome is unlikely to generalize well and the resulting recognition system may fail to deliver the expectable performance. In many tasks, it is therefore crucial to employ additional mechanisms of making the feature selection process more stable and resistant the curse of dimensionality effects. In this paper we discuss three different approaches to reducing this problem.
dcterms:title
Improving feature selection resistance to failures caused by curse-of-dimensionality Improving feature selection resistance to failures caused by curse-of-dimensionality
skos:prefLabel
Improving feature selection resistance to failures caused by curse-of-dimensionality Improving feature selection resistance to failures caused by curse-of-dimensionality
skos:notation
RIV/67985556:_____/11:00368741!RIV12-AV0-67985556
n12:predkladatel
n19:ico%3A67985556
n3:aktivita
n16:P n16:Z
n3:aktivity
P(1M0572), P(2C06019), P(GA102/08/0593), Z(AV0Z10750506)
n3:cisloPeriodika
3
n3:dodaniDat
n15:2012
n3:domaciTvurceVysledku
n11:6617972 n11:6956432 n11:5728525
n3:druhVysledku
n13:J
n3:duvernostUdaju
n18:S
n3:entitaPredkladatele
n8:predkladatel
n3:idSjednocenehoVysledku
203968
n3:idVysledku
RIV/67985556:_____/11:00368741
n3:jazykVysledku
n10:eng
n3:klicovaSlova
feature selection; curse of dimensionality; over-fitting; stability; machine learning; dimensionality reduction
n3:klicoveSlovo
n4:curse%20of%20dimensionality n4:machine%20learning n4:stability n4:dimensionality%20reduction n4:over-fitting n4:feature%20selection
n3:kodStatuVydavatele
CZ - Česká republika
n3:kontrolniKodProRIV
[24F2A8535D6D]
n3:nazevZdroje
Kybernetika
n3:obor
n14:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
4
n3:projekt
n9:GA102%2F08%2F0593 n9:2C06019 n9:1M0572
n3:rokUplatneniVysledku
n15:2011
n3:svazekPeriodika
47
n3:tvurceVysledku
Grim, Jiří Novovičová, Jana Somol, Petr Pudil, P.
n3:wos
000293207900007
n3:zamer
n5:AV0Z10750506
s:issn
0023-5954
s:numberOfPages
25