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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F10%3A00348726%21RIV11-MSM-67985556
rdf:type
n8:Vysledek skos:Concept
dcterms:description
Stability (robustness) of feature selection methods is a topic of recent interest, yet often neglected importance, with direct impact on the reliability of machine learning systems. We investigate the problem of evaluating the stability of feature selection processes yielding subsets of varying size. We introduce several novel feature selection stability measures and adjust some existing measures in a unifying framework that offers broad insight into the stability problem. We study in detail the properties of considered measures and demonstrate on various examples what information about the feature selection process can be gained. We also introduce an alternative approach to feature selection evaluation in the form of measures that enable comparing the similarity of two feature selection processes. These measures enable comparing, e.g., the output of two feature selection methods or two runs of one method with different parameters. Stability (robustness) of feature selection methods is a topic of recent interest, yet often neglected importance, with direct impact on the reliability of machine learning systems. We investigate the problem of evaluating the stability of feature selection processes yielding subsets of varying size. We introduce several novel feature selection stability measures and adjust some existing measures in a unifying framework that offers broad insight into the stability problem. We study in detail the properties of considered measures and demonstrate on various examples what information about the feature selection process can be gained. We also introduce an alternative approach to feature selection evaluation in the form of measures that enable comparing the similarity of two feature selection processes. These measures enable comparing, e.g., the output of two feature selection methods or two runs of one method with different parameters.
dcterms:title
Evaluating Stability and Comparing Output of Feature Selectors that Optimize Feature Subset Cardinality Evaluating Stability and Comparing Output of Feature Selectors that Optimize Feature Subset Cardinality
skos:prefLabel
Evaluating Stability and Comparing Output of Feature Selectors that Optimize Feature Subset Cardinality Evaluating Stability and Comparing Output of Feature Selectors that Optimize Feature Subset Cardinality
skos:notation
RIV/67985556:_____/10:00348726!RIV11-MSM-67985556
n3:aktivita
n13:Z n13:P
n3:aktivity
P(1M0572), P(2C06019), P(GA102/07/1594), P(GA102/08/0593), Z(AV0Z10750506)
n3:cisloPeriodika
11
n3:dodaniDat
n10:2011
n3:domaciTvurceVysledku
n12:6956432 n12:6617972
n3:druhVysledku
n16:J
n3:duvernostUdaju
n15:S
n3:entitaPredkladatele
n11:predkladatel
n3:idSjednocenehoVysledku
257776
n3:idVysledku
RIV/67985556:_____/10:00348726
n3:jazykVysledku
n4:eng
n3:klicovaSlova
feature selection; feature stability; stability measures; similarity measures; sequential search; individual ranking; feature subset-size optimization; high dimensionality; small sample size
n3:klicoveSlovo
n6:small%20sample%20size n6:similarity%20measures n6:high%20dimensionality n6:feature%20selection n6:feature%20stability n6:feature%20subset-size%20optimization n6:individual%20ranking n6:sequential%20search n6:stability%20measures
n3:kodStatuVydavatele
US - Spojené státy americké
n3:kontrolniKodProRIV
[51A084BA282A]
n3:nazevZdroje
IEEE Transactions on Pattern Analysis and Machine Intelligence
n3:obor
n18:BD
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n5:GA102%2F07%2F1594 n5:GA102%2F08%2F0593 n5:2C06019 n5:1M0572
n3:rokUplatneniVysledku
n10:2010
n3:svazekPeriodika
32
n3:tvurceVysledku
Novovičová, Jana Somol, Petr
n3:wos
000281990900001
n3:zamer
n17:AV0Z10750506
s:issn
0162-8828
s:numberOfPages
19