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Statements

Subject Item
n2:RIV%2F61989592%3A15310%2F10%3A10216519%21RIV11-GA0-15310___
rdf:type
skos:Concept n21:Vysledek
dcterms:description
The paper presents an utilization of formal concept analysis in input data preprocessing for machine learning. Two preprocessing methods are presented. 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. In both methods the new attributes are defined by certain formal concepts computed from input data table. Selected formal concepts are so-called factor concepts obtained by boolean factor analysis, recently described by FCA. The ML method used to demonstrate the ideas is 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. The paper presents an utilization of formal concept analysis in input data preprocessing for machine learning. Two preprocessing methods are presented. 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. In both methods the new attributes are defined by certain formal concepts computed from input data table. Selected formal concepts are so-called factor concepts obtained by boolean factor analysis, recently described by FCA. The ML method used to demonstrate the ideas is 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
Preprocessing input data for machine learning by FCA Preprocessing input data for machine learning by FCA
skos:prefLabel
Preprocessing input data for machine learning by FCA Preprocessing input data for machine learning by FCA
skos:notation
RIV/61989592:15310/10:10216519!RIV11-GA0-15310___
n3:aktivita
n16:P
n3:aktivity
P(GPP202/10/P360)
n3:dodaniDat
n6:2011
n3:domaciTvurceVysledku
n14:1865994
n3:druhVysledku
n11:D
n3:duvernostUdaju
n18:S
n3:entitaPredkladatele
n4:predkladatel
n3:idSjednocenehoVysledku
281422
n3:idVysledku
RIV/61989592:15310/10:10216519
n3:jazykVysledku
n12:eng
n3:klicovaSlova
formal concept analysis; matrix decomposition; decision trees; machine learning; data preprocessing
n3:klicoveSlovo
n7:decision%20trees n7:matrix%20decomposition n7:machine%20learning n7:formal%20concept%20analysis n7:data%20preprocessing
n3:kontrolniKodProRIV
[D999576B1478]
n3:mistoKonaniAkce
Sevilla, Španělsko
n3:mistoVydani
Sevilla
n3:nazevZdroje
Proceedings of the 7th International Conference on Concept Lattices and Their Applications
n3:obor
n17:IN
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:projekt
n13:GPP202%2F10%2FP360
n3:rokUplatneniVysledku
n6:2010
n3:tvurceVysledku
OUTRATA, Jan
n3:typAkce
n19:WRD
n3:zahajeniAkce
2010-10-19+02:00
s:numberOfPages
12
n9:hasPublisher
University of Sevilla
n8:isbn
978-84-614-4027-6
n20:organizacniJednotka
15310