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Values Deletion to Improve Deep Imputation Processes

  • Adrián Sánchez-MoralesEmail author
  • José-Luis Sancho-Gómez
  • Aníbal R. Figueiras-Vidal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Imputation Method Deep Neural Network Imputation Procedure Imputation Technique Noisy Version 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Adrián Sánchez-Morales
    • 1
    Email author
  • José-Luis Sancho-Gómez
    • 1
  • Aníbal R. Figueiras-Vidal
    • 2
  1. 1.Department of Information and Communications TechnologiesUniversidad Politécnica de CartagenaCartagena (Murcia)Spain
  2. 2.Signal Theory and Communications DepartmentUniversidad Carlos III de MadridMadridSpain

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