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Scalable Model-Based Cascaded Imputation of Missing Data

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Advances in Knowledge Discovery and Data Mining (PAKDD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10939))

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Abstract

Missing data is a common trait of real-world data that can negatively impact interpretability. In this paper, we present Cascade Imputation (CIM), an effective and scalable technique for automatic imputation of missing data. CIM is not restrictive on the characteristics of the data set, providing support for: Missing At Random and Missing Completely At Random data, numerical and nominal attributes, and large data sets including highly dimensional data sets. We compare CIM against well-established imputation techniques over a variety of data sets under multiple test configurations to measure the impact of imputation on the classification problem. Test results show that CIM outperforms other imputation methods over multiple test conditions. Additionally, we identify optimal performance and failure conditions for popular imputation techniques.

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Author information

Authors and Affiliations

  1. LTCI, Télécom ParisTech, Université Paris-Saclay, 75013, Paris, France

    Jacob Montiel, Albert Bifet & Talel Abdessalem

  2. LIX, École Polytechnique, 91120, Palaiseau, France

    Jesse Read

  3. UMI CNRS IPAL & National University of Singapore, Singapore, Singapore

    Talel Abdessalem

Authors
  1. Jacob Montiel
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  2. Jesse Read
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  3. Albert Bifet
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  4. Talel Abdessalem
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Corresponding author

Correspondence to Jacob Montiel .

Editor information

Editors and Affiliations

  1. Deakin University, Geelong, Victoria, Australia

    Dinh Phung

  2. National Chiao Tung University, Hsinchu City, Taiwan

    Vincent S. Tseng

  3. Monash University, Clayton, Victoria, Australia

    Geoffrey I. Webb

  4. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Bao Ho

  5. University of Melbourne, Melbourne, Victoria, Australia

    Mohadeseh Ganji

  6. University of Melbourne, Melbourne, Victoria, Australia

    Lida Rashidi

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Montiel, J., Read, J., Bifet, A., Abdessalem, T. (2018). Scalable Model-Based Cascaded Imputation of Missing Data. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_6

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  • DOI: https://doi.org/10.1007/978-3-319-93040-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93039-8

  • Online ISBN: 978-3-319-93040-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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