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Values Deletion to Improve Deep Imputation Processes

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Biomedical Applications Based on Natural and Artificial Computing (IWINAC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10338))

Abstract

Most machine learning algorithms are based on the assumption that available data are completely known, nevertheless, real world data sets are often incomplete. For this reason, the ability of handling missing values has become a fundamental requirement for statistical pattern recognition. In this article, a new proposal to impute missing values with deep networks is analyzed. Besides the real missing values, the method introduces a percentage of artificial missing (‘deleted values’) using the true values as targets. Empirical results over several UCI repository datasets show that this method is able to improve the final imputed values obtained by other procedures used as pre-imputation.

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Author information

Authors and Affiliations

  1. Department of Information and Communications Technologies, Universidad Politécnica de Cartagena, Plaza del Hospital, 1, 30202, Cartagena (Murcia), Spain

    Adrián Sánchez-Morales & José-Luis Sancho-Gómez

  2. Signal Theory and Communications Department, Universidad Carlos III de Madrid, Madrid, Spain

    Aníbal R. Figueiras-Vidal

Authors
  1. Adrián Sánchez-Morales
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  2. José-Luis Sancho-Gómez
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  3. Aníbal R. Figueiras-Vidal
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Corresponding author

Correspondence to Adrián Sánchez-Morales .

Editor information

Editors and Affiliations

  1. Departamento de Electrónica, Tecnología de Computadoras y Proyectos, Universidad Politécnica de Cartagena, Cartagena, Spain

    José Manuel Ferrández Vicente

  2. Departamento de Inteligencia Articial, Universidad Nacional de Educación a Distancia, Madrid, Spain

    José Ramón Álvarez-Sánchez

  3. Departamento de Inteligencia Articial, Universidad Nacional de Educación a Distancia, Madrid, Spain

    Félix de la Paz López

  4. Departamento de Electrónica, Tecnología de Computadoras y Proyectos, Universidad Politécnica de Cartagena, Cartagena, Spain

    Javier Toledo Moreo

  5. The Ohio State University, Columbus, Ohio, USA

    Hojjat Adeli

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Sánchez-Morales, A., Sancho-Gómez, JL., Figueiras-Vidal, A.R. (2017). Values Deletion to Improve Deep Imputation Processes. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Biomedical Applications Based on Natural and Artificial Computing. IWINAC 2017. Lecture Notes in Computer Science(), vol 10338. Springer, Cham. https://doi.org/10.1007/978-3-319-59773-7_25

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  • DOI: https://doi.org/10.1007/978-3-319-59773-7_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59772-0

  • Online ISBN: 978-3-319-59773-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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