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Dealing with Missing Values

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Data Preprocessing in Data Mining

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 72))

Abstract

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.

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Authors and Affiliations

  1. Department of Computer Science, University of Jaén, Jaén, Spain

    Salvador García

  2. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Julián Luengo

  3. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Francisco Herrera

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  1. Salvador García
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  3. Francisco Herrera
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García, S., Luengo, J., Herrera, F. (2015). Dealing with Missing Values. In: Data Preprocessing in Data Mining. Intelligent Systems Reference Library, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-319-10247-4_4

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