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Global Induction of Oblique Survival Trees

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Computational Science – ICCS 2024 (ICCS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14835))

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Abstract

Survival analysis focuses on the prediction of failure time and serves as an important prognostic tool, not solely confined to medicine but also across diverse fields. Machine learning methods, especially decision trees, are increasingly replacing traditional statistical methods which are based on assumptions that are often difficult to meet. The paper presents a new global method for inducing survival trees containing Kaplan–Mayer estimators in leaves. Using a specialized evolutionary algorithm, the method searches for oblique trees in which multivariate tests in internal nodes divide the feature space using hyperplanes. Specific variants of mutation and crossover operators have been developed, making evolution effective and efficient. The fitness function is based on the integrated Brier score and prevents overfitting taking into account the size of the tree. A preliminary experimental verification and comparison with classical univariate trees was carried out on real medical datasets. The evaluation results are promising.

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Acknowledgement

This work was supported by Bialystok University of Technology under the grant WZ/WI-IIT/4/2023 founded by Ministry of Science and Higher Education.

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Authors and Affiliations

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

    Malgorzata Kretowska & Marek Kretowski

Authors
  1. Malgorzata Kretowska
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  2. Marek Kretowski
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Correspondence to Malgorzata Kretowska .

Editor information

Editors and Affiliations

  1. University of Malaga, Malaga, Spain

    Leonardo Franco

  2. University of Amsterdam, Amsterdam, The Netherlands

    Clélia de Mulatier

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

    Maciej Paszynski

  4. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  5. University of Tennessee, Knoxville, TN, USA

    Jack J. Dongarra

  6. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

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Kretowska, M., Kretowski, M. (2024). Global Induction of Oblique Survival Trees. In: Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2024. ICCS 2024. Lecture Notes in Computer Science, vol 14835. Springer, Cham. https://doi.org/10.1007/978-3-031-63772-8_33

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  • DOI: https://doi.org/10.1007/978-3-031-63772-8_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-63771-1

  • Online ISBN: 978-3-031-63772-8

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