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Statements

Subject Item
n2:RIV%2F61989592%3A15310%2F10%3A10216523%21RIV11-GA0-15310___
rdf:type
skos:Concept n13:Vysledek
dcterms:description
We present two input data preprocessing methods for machine learning (ML). The first one consists in extending the set of attributes describing objects in input data table by new attributes and the second one consists in replacing the attributes by new attributes. The methods utilize formal concept analysis (FCA) and boolean factor analysis, recently described by FCA, in that the new attributes are defined by so-called factor concepts computed from input data table. The methods are demonstrated on decision tree induction. The experimental evaluation and comparison of performance of decision trees induced from original and preprocessed input data is performed with standard decision tree induction algorithms ID3 and C4.5 on several benchmark datasets. We present two input data preprocessing methods for machine learning (ML). The first one consists in extending the set of attributes describing objects in input data table by new attributes and the second one consists in replacing the attributes by new attributes. The methods utilize formal concept analysis (FCA) and boolean factor analysis, recently described by FCA, in that the new attributes are defined by so-called factor concepts computed from input data table. The methods are demonstrated on decision tree induction. The experimental evaluation and comparison of performance of decision trees induced from original and preprocessed input data is performed with standard decision tree induction algorithms ID3 and C4.5 on several benchmark datasets.
dcterms:title
Boolean factor analysis for data preprocessing in machine learning Boolean factor analysis for data preprocessing in machine learning
skos:prefLabel
Boolean factor analysis for data preprocessing in machine learning Boolean factor analysis for data preprocessing in machine learning
skos:notation
RIV/61989592:15310/10:10216523!RIV11-GA0-15310___
n4:aktivita
n17:P
n4:aktivity
P(GPP202/10/P360)
n4:dodaniDat
n8:2011
n4:domaciTvurceVysledku
n20:1865994
n4:druhVysledku
n21:D
n4:duvernostUdaju
n15:S
n4:entitaPredkladatele
n14:predkladatel
n4:idSjednocenehoVysledku
249131
n4:idVysledku
RIV/61989592:15310/10:10216523
n4:jazykVysledku
n18:eng
n4:klicovaSlova
formal concept; matrix decomposition; decision trees; machine learning; data preprocessing
n4:klicoveSlovo
n5:machine%20learning n5:matrix%20decomposition n5:data%20preprocessing n5:decision%20trees n5:formal%20concept
n4:kontrolniKodProRIV
[098262182131]
n4:mistoKonaniAkce
Washington, D.C., USA
n4:mistoVydani
Washington DC
n4:nazevZdroje
Proceedings of ICMLA 2010
n4:obor
n9:IN
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
1
n4:projekt
n19:GPP202%2F10%2FP360
n4:rokUplatneniVysledku
n8:2010
n4:tvurceVysledku
OUTRATA, Jan
n4:typAkce
n12:WRD
n4:zahajeniAkce
2010-12-12+01:00
s:numberOfPages
4
n16:hasPublisher
IEEE
n10:isbn
978-0-7695-4300-0
n11:organizacniJednotka
15310