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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F07%3A03132900%21RIV08-AV0-21230___
rdf:type
n6:Vysledek skos:Concept
dcterms:description
Missing data is a big problem in simulation for data mining and data analysis. Real world applications often contains missing data. Many data-mining methods is unable to create models from data which contains missing values. Traditional approach is to delete vectors with missing data. Unfortunately, this approach may lead to decreased accuracy of the models and in the worst case all data in dataset may be deleted. For this reason many different imputation techniques were developed and some are widely used. In this paper, we present a comparison of several well-known techniques for missing data imputation. Presented techniques includes imputation of mean value, zero, value from nearest input vector and few others. In this paper we show which techniques are the best in estimation of missing values. To test imputation methods we used several different datasets. We compare the imputation methods in two ways. The first is to compare imputed data with original data. Missing data is a big problem in simulation for data mining and data analysis. Real world applications often contains missing data. Many data-mining methods is unable to create models from data which contains missing values. Traditional approach is to delete vectors with missing data. Unfortunately, this approach may lead to decreased accuracy of the models and in the worst case all data in dataset may be deleted. For this reason many different imputation techniques were developed and some are widely used. In this paper, we present a comparison of several well-known techniques for missing data imputation. Presented techniques includes imputation of mean value, zero, value from nearest input vector and few others. In this paper we show which techniques are the best in estimation of missing values. To test imputation methods we used several different datasets. We compare the imputation methods in two ways. The first is to compare imputed data with original data. Missing data is a big problem in simulation for data mining and data analysis. Real world applications often contains missing data. Many data-mining methods is unable to create models from data which contains missing values. Traditional approach is to delete vectors with missing data. Unfortunately, this approach may lead to decreased accuracy of the models and in the worst case all data in dataset may be deleted. For this reason many different imputation techniques were developed and some are widely used. In this paper, we present a comparison of several well-known techniques for missing data imputation. Presented techniques includes imputation of mean value, zero, value from nearest input vector and few others. In this paper we show which techniques are the best in estimation of missing values. To test imputation methods we used several different datasets. We compare the imputation methods in two ways. The first is to compare imputed data with original data.
dcterms:title
Missing Data Imputation and the Inductive Modelling Nahrazování chybějících dat a induktivní modelování Missing Data Imputation and the Inductive Modelling
skos:prefLabel
Missing Data Imputation and the Inductive Modelling Nahrazování chybějících dat a induktivní modelování Missing Data Imputation and the Inductive Modelling
skos:notation
RIV/68407700:21230/07:03132900!RIV08-AV0-21230___
n3:strany
Nečíslováno
n3:aktivita
n16:Z n16:P
n3:aktivity
P(KJB201210701), Z(MSM6840770012)
n3:dodaniDat
n4:2008
n3:domaciTvurceVysledku
n9:3271404 n9:7035586 n9:1266500
n3:druhVysledku
n22:D
n3:duvernostUdaju
n14:S
n3:entitaPredkladatele
n18:predkladatel
n3:idSjednocenehoVysledku
434037
n3:idVysledku
RIV/68407700:21230/07:03132900
n3:jazykVysledku
n21:eng
n3:klicovaSlova
GAME Neural Network; Inductive modelling method; Missing data imputation; Missong data
n3:klicoveSlovo
n17:Missing%20data%20imputation n17:GAME%20Neural%20Network n17:Missong%20data n17:Inductive%20modelling%20method
n3:kontrolniKodProRIV
[77B9EAB9C137]
n3:mistoKonaniAkce
Ljubljana
n3:mistoVydani
Vienna
n3:nazevZdroje
Proceedings of the 6th EUROSIM Congress on Modelling and Simulation
n3:obor
n8:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n5:KJB201210701
n3:rokUplatneniVysledku
n4:2007
n3:tvurceVysledku
Šnorek, Miroslav Čepek, Miroslav Kordík, Pavel
n3:typAkce
n10:WRD
n3:zahajeniAkce
2007-09-09+02:00
n3:zamer
n20:MSM6840770012
s:numberOfPages
8
n13:hasPublisher
ARGESIM
n11:isbn
978-3-901608-32-2
n15:organizacniJednotka
21230