Dealing with Missing Values

  • Salvador GarcíaEmail author
  • Julián Luengo
  • Francisco Herrera
Part of the Intelligent Systems Reference Library book series (ISRL, volume 72)


In this chapter the reader is introduced to the approaches used in the literature to tackle the presence of Missing Values (MVs). In real-life data, information is frequently lost in data mining, caused by the presence of missing values in attributes. Several schemes have been studied to overcome the drawbacks produced by missing values in data mining tasks; one of the most well known is based on preprocessing, formally known as imputation. After the introduction in Sect. 4.1, the chapter begins with the theoretical background which analyzes the underlying distribution of the missingness in Sect. 4.2. From this point on, the successive sections go from the simplest approaches in Sect. 4.3, to the most advanced proposals, focusing in the imputation of the MVs. The scope of such advanced methods includes the classic maximum likelihood procedures, like Expectation-Maximization or Multiple-Imputation (Sect. 4.4) and the latest Machine Learning based approaches which use algorithms for classification or regression in order to accomplish the imputation (Sect. 4.5). Finally a comparative experimental study will be carried out in Sect. 4.6.


Multiple Imputation Imputation Method Principal Component Regression Input Attribute Data Augmentation 
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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© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Salvador García
    • 1
    Email author
  • Julián Luengo
    • 2
  • Francisco Herrera
    • 3
  1. 1.Department of Computer ScienceUniversity of JaénJaénSpain
  2. 2.Department of Civil EngineeringUniversity of BurgosBurgosSpain
  3. 3.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

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