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Outliers Detection in Regressions by Nonparametric Parzen Kernel Estimation

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Artificial Intelligence and Soft Computing (ICAISC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10842))

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Abstract

A certain observation which is unusual or different from all other ones is called the outlier or anomaly. Appropriate evaluation of data is a crucial problem in modelling of the real objects or phenomena. Actually investigated problems often are based on data mass-produced by computer systems, without careful inspection or screening. The great amount of generated and processed information (e.g. so-called Big-Data) cause that possible outliers often go unnoticed and the result is that they can be masked. However, in regression, this situation can be more complicated. The identification and evaluation of the extremely atypical measurements in observations, for instance in some areas of medicine, geology, particularly in seismology (earthquakes), is precisely the outliers that are the subjects of interest. In this paper, a nonparametric procedure based on Parzen kernel for estimation of unknown function is applied. Evaluation of which measurements in input data-set could be recognized as outliers and possibly should be removed has been performed using the Cook’s Distance formula. Anomaly detection is still an important problem to be researched within diverse areas and application domains.

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Author information

Authors and Affiliations

  1. Institute of Computational Intelligence, Czestochowa University of Technology, Czestochowa, Poland

    Tomasz Galkowski

  2. Information Technology Institute, University of Social Sciences, 90-113, Lodz, Poland

    Andrzej Cader

  3. Clark University, Worcester, MA, 01610, USA

    Andrzej Cader

Authors
  1. Tomasz Galkowski
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  2. Andrzej Cader
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Corresponding author

Correspondence to Tomasz Galkowski .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Galkowski, T., Cader, A. (2018). Outliers Detection in Regressions by Nonparametric Parzen Kernel Estimation. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10842. Springer, Cham. https://doi.org/10.1007/978-3-319-91262-2_32

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  • DOI: https://doi.org/10.1007/978-3-319-91262-2_32

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