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| rdf:type
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| 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. (en)
- 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. (cs)
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| Title
| - Missing Data Imputation and the Inductive Modelling
- Nahrazování chybějících dat a induktivní modelování (cs)
- Missing Data Imputation and the Inductive Modelling (en)
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| skos:prefLabel
| - Missing Data Imputation and the Inductive Modelling
- Nahrazování chybějících dat a induktivní modelování (cs)
- Missing Data Imputation and the Inductive Modelling (en)
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| skos:notation
| - RIV/68407700:21230/07:03132900!RIV08-AV0-21230___
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| http://linked.open.../vavai/riv/strany
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
| - P(KJB201210701), Z(MSM6840770012)
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/07:03132900
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - GAME Neural Network; Inductive modelling method; Missing data imputation; Missong data (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...v/mistoKonaniAkce
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| http://linked.open...i/riv/mistoVydani
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| http://linked.open...i/riv/nazevZdroje
| - Proceedings of the 6th EUROSIM Congress on Modelling and Simulation
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| http://linked.open...in/vavai/riv/obor
| |
| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
| |
| http://linked.open...vavai/riv/projekt
| |
| http://linked.open...UplatneniVysledku
| |
| http://linked.open...iv/tvurceVysledku
| - Čepek, Miroslav
- Kordík, Pavel
- Šnorek, Miroslav
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| http://linked.open...vavai/riv/typAkce
| |
| http://linked.open.../riv/zahajeniAkce
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| http://linked.open...n/vavai/riv/zamer
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| number of pages
| |
| http://purl.org/ne...btex#hasPublisher
| |
| https://schema.org/isbn
| |
| http://localhost/t...ganizacniJednotka
| |
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