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Investigation of the Impact of Missing Value Imputation Methods on the k-NN Classification Accuracy

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Computational Collective Intelligence

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9330))

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Abstract

Paper desribes results of an experiment where various scenarios of missing values occurrence in the data repository has been tested. Experiment was coducted on a publicly available database, containing complete, multidimensional continuous dataspace and multiple classes. Missing values were introduced using “completely at random” scheme. Tested scenarios were: training and testing using incomplete dataset, training on complete data set and testing on incomplete and vice versa. For comparison to data imputation methods also the ensemble of single-feature kNN classifiers, working withoud data imputation, has been tested.

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Authors and Affiliations

  1. Institute of Computer Science, University of Silesia in Katowice, Bedzinska 39, Sosnowiec, Poland

    Tomasz Orczyk & Piotr Porwik

Authors
  1. Tomasz Orczyk
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  2. Piotr Porwik
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Corresponding author

Correspondence to Tomasz Orczyk .

Editor information

Editors and Affiliations

  1. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Núñez

  2. Wroclaw University of Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  3. Computer Science Department, Universidad Autónoma De Madrid, Madrid, Spain

    David Camacho

  4. Wroclaw University of Technology, Wroclaw, Poland

    Bogdan Trawiński

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Orczyk, T., Porwik, P. (2015). Investigation of the Impact of Missing Value Imputation Methods on the k-NN Classification Accuracy. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9330. Springer, Cham. https://doi.org/10.1007/978-3-319-24306-1_54

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  • DOI: https://doi.org/10.1007/978-3-319-24306-1_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24305-4

  • Online ISBN: 978-3-319-24306-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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