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Implementation of Ensemble Self-Organizing Maps for Missing Values Imputation.

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Τίτλος: Implementation of Ensemble Self-Organizing Maps for Missing Values Imputation.
Συγγραφείς: Siswantining, Titin, Vivaldi, Kathan Gerry, Sarwinda, Devvi, Soemartojo, Saskya Mary, Sari, Ika Marta, Al-Ash, Herley Shaori
Πηγή: Indonesian Journal of Statistics & Its Applications; May2022, Vol. 6 Issue 1, p1-12, 12p
Θεματικοί όροι: Self-organizing maps, Electronic data processing, Generalization, Random forest algorithms, Data analysis, Iterative methods (Mathematics)
Περίληψη: The purpose of this study is to implement the ensemble self-organizing maps (ESOM) method to impute missing values at the preprocessing data stage, which is an important stage when making predictions or classifications. The Ensemble Self Organizing Maps (E-SOM) is the development of the SOM imputation method, in which the E-SOM method is implemented by applying an ensemble framework using several SOMs to improve generalization capabilities. In this study, the E-SOM imputation method is implemented in South African heart disease data using random forest as a classification model. The results of the model evaluation showed that for accuracy in testing data, the Random Forest model formed from E-SOM imputed data yields better accuracy values than the Random Forest model formed from SOM-imputed data for variations of 36, 49, 64, and 81 neurons, while for variation of 25 neurons both models produce the same accuracy value. From the variation of the number of ensembles applied, the E-SOM imputation method with a combination of 81 neurons and 15 ensemble numbers produced a Random Forest model with the most optimal value of accuracy. [ABSTRACT FROM AUTHOR]
Copyright of Indonesian Journal of Statistics & Its Applications is the property of IPB University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Implementation of Ensemble Self-Organizing Maps for Missing Values Imputation.
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  Data: <searchLink fieldCode="AR" term="%22Siswantining%2C+Titin%22">Siswantining, Titin</searchLink><br /><searchLink fieldCode="AR" term="%22Vivaldi%2C+Kathan+Gerry%22">Vivaldi, Kathan Gerry</searchLink><br /><searchLink fieldCode="AR" term="%22Sarwinda%2C+Devvi%22">Sarwinda, Devvi</searchLink><br /><searchLink fieldCode="AR" term="%22Soemartojo%2C+Saskya+Mary%22">Soemartojo, Saskya Mary</searchLink><br /><searchLink fieldCode="AR" term="%22Sari%2C+Ika+Marta%22">Sari, Ika Marta</searchLink><br /><searchLink fieldCode="AR" term="%22Al-Ash%2C+Herley+Shaori%22">Al-Ash, Herley Shaori</searchLink>
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  Data: Indonesian Journal of Statistics & Its Applications; May2022, Vol. 6 Issue 1, p1-12, 12p
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  Data: <searchLink fieldCode="DE" term="%22Self-organizing+maps%22">Self-organizing maps</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Generalization%22">Generalization</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Iterative+methods+%28Mathematics%29%22">Iterative methods (Mathematics)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The purpose of this study is to implement the ensemble self-organizing maps (ESOM) method to impute missing values at the preprocessing data stage, which is an important stage when making predictions or classifications. The Ensemble Self Organizing Maps (E-SOM) is the development of the SOM imputation method, in which the E-SOM method is implemented by applying an ensemble framework using several SOMs to improve generalization capabilities. In this study, the E-SOM imputation method is implemented in South African heart disease data using random forest as a classification model. The results of the model evaluation showed that for accuracy in testing data, the Random Forest model formed from E-SOM imputed data yields better accuracy values than the Random Forest model formed from SOM-imputed data for variations of 36, 49, 64, and 81 neurons, while for variation of 25 neurons both models produce the same accuracy value. From the variation of the number of ensembles applied, the E-SOM imputation method with a combination of 81 neurons and 15 ensemble numbers produced a Random Forest model with the most optimal value of accuracy. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Indonesian Journal of Statistics & Its Applications is the property of IPB University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.29244/ijsa.v6i1p1-12
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 12
        StartPage: 1
    Subjects:
      – SubjectFull: Self-organizing maps
        Type: general
      – SubjectFull: Electronic data processing
        Type: general
      – SubjectFull: Generalization
        Type: general
      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Data analysis
        Type: general
      – SubjectFull: Iterative methods (Mathematics)
        Type: general
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      – TitleFull: Implementation of Ensemble Self-Organizing Maps for Missing Values Imputation.
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            NameFull: Siswantining, Titin
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            NameFull: Vivaldi, Kathan Gerry
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            NameFull: Sarwinda, Devvi
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            NameFull: Soemartojo, Saskya Mary
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            NameFull: Sari, Ika Marta
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            NameFull: Al-Ash, Herley Shaori
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            – D: 01
              M: 05
              Text: May2022
              Type: published
              Y: 2022
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            – TitleFull: Indonesian Journal of Statistics & Its Applications
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