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Local Data Characteristics in Learning Classifiers from Imbalanced Data

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Advances in Data Analysis with Computational Intelligence Methods

Part of the book series: Studies in Computational Intelligence ((SCI,volume 738))

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Abstract

Learning classifiers from imbalanced data is still one of challenging tasks in machine learning and data mining. Data difficulty factors referring to internal and local characteristics of class distributions deteriorate performance of standard classifiers. Many of these factors may be approximated by analyzing the neighbourhood of the learning examples and identifying different types of examples from the minority class. In this paper, we follow recent research on developing such methods for assessing the types of examples which exploit either k-nearest neighbours or kernels. We discuss the approaches to tune the size of both kinds of neighborhoods depending on the data set characteristics and evaluate their usefulness in series of experiments with real-world and synthetic data sets. Furthermore, we claim that the proper analysis of these neighborhoods could be the basis for developing new specialized algorithms for imbalanced data. To illustrate it, we study generalizations of over-sampling in pre-processing methods and neighbourhood based ensembles.

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Notes

  1. 1.

    http://www.ics.uci.edu/mlearn/MLRepository.html.

  2. 2.

    We are grateful to Prof. W. Michalowski and the MET Research Group from the University of Ottawa for providing us an access to scrotal-pain data set.

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Acknowledgements

The research was funded by the the Polish National Science Center, grant no. DEC-2013/11/B/ST6/00963. Close co-operation with Krystyna Napierala in research on modeling types of examples and with Mateusz Lango in research on ensemble models is also acknowledged.

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Authors and Affiliations

  1. Institute of Computing Science, Poznań University of Technology, Piotrowo 2, 60-965, Poznań, Poland

    Jerzy Błaszczyński & Jerzy Stefanowski

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  1. Jerzy Błaszczyński
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  2. Jerzy Stefanowski
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Correspondence to Jerzy Stefanowski .

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Editors and Affiliations

  1. Department of Medicine, Division of Nephrology and Hypertension, University of Louisville, Louisville, Kentucky, USA

    Adam E Gawęda

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  3. Institute of Computational Intelligence, Department of Mechanical Engineering and Computer Science, Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  4. School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, Oklahoma, USA

    Gary G. Yen

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Błaszczyński, J., Stefanowski, J. (2018). Local Data Characteristics in Learning Classifiers from Imbalanced Data. In: Gawęda, A., Kacprzyk, J., Rutkowski, L., Yen, G. (eds) Advances in Data Analysis with Computational Intelligence Methods. Studies in Computational Intelligence, vol 738. Springer, Cham. https://doi.org/10.1007/978-3-319-67946-4_2

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