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Hybrid Splitting Criterion in Decision Trees for Data Stream Mining

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Book cover Artificial Intelligence and Soft Computing (ICAISC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9693))

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Abstract

In this paper the issue of splitting criteria used in decision tree induction algorithm designed for data streams is analyzed. A hybrid splitting criterion is proposed which combines two criteria established for two different split measure functions: the Gini gain and the split measure based on the misclassification error. The hybrid splitting criterion reveals advantages of its both component. The online decision tree with hybrid criterion demonstrates higher classification accuracy than the online decision trees with both considered single criteria.

M. Pawlak carried out this research at USS during his sabbatical leave from University of Manitoba.

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Acknowledgments

This work was supported by the Polish National Science Center under Grant No. 2014/13/N/ST6/01848.

Author information

Authors and Affiliations

  1. Institute of Computational Intelligence, Czestochowa University of Technology, Armii Krajowej 36, 42-200, Czestochowa, Poland

    Maciej Jaworski & Leszek Rutkowski

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

    Leszek Rutkowski & Miroslaw Pawlak

  3. Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Canada

    Miroslaw Pawlak

Authors
  1. Maciej Jaworski
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  2. Leszek Rutkowski
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  3. Miroslaw Pawlak
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Corresponding author

Correspondence to Maciej Jaworski .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Czestochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Czestochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Czestochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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Jaworski, M., Rutkowski, L., Pawlak, M. (2016). Hybrid Splitting Criterion in Decision Trees for Data Stream Mining. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9693. Springer, Cham. https://doi.org/10.1007/978-3-319-39384-1_6

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  • DOI: https://doi.org/10.1007/978-3-319-39384-1_6

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