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Influence of Outliers Introduction on Predictive Models Quality

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Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery (BDAS 2015, BDAS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 613))

Abstract

The paper presents results of the research related to influence of the level of outliers in the data (train and test data considered separately) on the quality of a model prediction in a classification task. The set of 100 semi–artificial time series was taken into consideration, which independent variables was close to real ones, observed in a underground coal mining environment and dependent variable was generated with the decision tree. For every considered method (decision trees, naive bayes, logistic regression and kNN) a reference model was built (no outliers in the data) which quality was compared with the quality of two models: Out–Out (outliers in train and test data) and Non-out–Out (outliers only in test data). 50 levels of outliers in the data were considered, from 1 % to 50 %. Statistical comparison of models was done on the basis of sign test.

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Acknowledgments

This work was partially supported by Polish National Centre for Research and Development (NCBiR) grant PBS2/B9/20/2013 in frame of Applied Research Programmes. The infrastructure was supported by “PL-LAB2020” project, contract POIG.02.03.01-00-104/13-00.

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Authors and Affiliations

  1. Institute of Fundamentals of Machinery Design, Silesian University of Technology, ul. Akademicka 16, 44-100, Gliwice, Poland

    Mateusz Kalisch & Piotr Przystałka

  2. Institute of Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100, Gliwice, Poland

    Marcin Michalak & Marek Sikora

  3. Institute of Innovative Technologies EMAG, ul. Leopolda 31, 40-186, Katowice, Poland

    Marcin Michalak, Marek Sikora & Łukasz Wróbel

Authors
  1. Mateusz Kalisch
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  2. Marcin Michalak
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  3. Marek Sikora
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  4. Łukasz Wróbel
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  5. Piotr Przystałka
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Corresponding author

Correspondence to Marcin Michalak .

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Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dariusz Mrozek

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Paweł Kasprowski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Bożena Małysiak-Mrozek

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Daniel Kostrzewa

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Kalisch, M., Michalak, M., Sikora, M., Wróbel, Ł., Przystałka, P. (2016). Influence of Outliers Introduction on Predictive Models Quality. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery. BDAS BDAS 2015 2016. Communications in Computer and Information Science, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-34099-9_5

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  • DOI: https://doi.org/10.1007/978-3-319-34099-9_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-34098-2

  • Online ISBN: 978-3-319-34099-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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