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Random Forest of Dipolar Trees for Survival Prediction

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Artificial Intelligence and Soft Computing – ICAISC 2006 (ICAISC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4029))

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Abstract

In the paper the method of using the ensemble of dipolar trees for survival prediction is presented. In the approach the random forest is applied to calculate the aggregated Kaplan-Meier survival function for a new patient. The induction of individual dipolar regression tree is based on minimization of a piece-wise linear criterion function. The algorithm allows using the information from censored observations for which the exact survival time is unknown. The Brier score is used to evaluate the prediction ability of the received model.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science, Białystok Technical University, Wiejska 45a, 15-351, Białystok, Poland

    Małgorzata Krętowska

Authors
  1. Małgorzata Krętowska
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Berkeley Initiative in Soft Computing (BISC), University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Żurada

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© 2006 Springer-Verlag Berlin Heidelberg

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Krętowska, M. (2006). Random Forest of Dipolar Trees for Survival Prediction. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_95

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  • DOI: https://doi.org/10.1007/11785231_95

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35748-3

  • Online ISBN: 978-3-540-35750-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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