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Comparison of Tree-Based Ensembles in Application to Censored Data

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Artificial Intelligence and Soft Computing (ICAISC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8467))

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Abstract

In the paper the comparison of ensemble based methods applied to censored survival data was conducted. Bagging survival trees, dipolar survival tree ensemble and random forest were taken into consideration. The prediction ability was evaluated by the integrated Brier score, the prediction measure developed for survival data. Two real datasets with different percentage of censored observations were examined.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science, Bialystok University of Technology, Wiejska 45a, 15-351, Bialystok, Poland

    Malgorzata Kretowska

Authors
  1. Malgorzata Kretowska
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Computer Science Division, Department of Electrical Engineering and Computer Sciences, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2014 Springer International Publishing Switzerland

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Kretowska, M. (2014). Comparison of Tree-Based Ensembles in Application to Censored Data. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8467. Springer, Cham. https://doi.org/10.1007/978-3-319-07173-2_47

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  • DOI: https://doi.org/10.1007/978-3-319-07173-2_47

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07172-5

  • Online ISBN: 978-3-319-07173-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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