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Smooth Sigmoid Surrogate (SSS): An Alternative to Greedy Search in Decision Trees

Author

Listed:
  • Xiaogang Su

    (Department of Mathematical Science, University of Texas at El Paso, El Paso, TX 79968, USA)

  • George Ekow Quaye

    (Division of Health Services and Outcomes Research, Children’s Mercy Kansas City, Kansas City, MO 64108, USA)

  • Yishu Wei

    (Reddit Inc., San Francisco, CA 94102, USA)

  • Joseph Kang

    (US Census Bureau, Washington, DC 20233, USA)

  • Lei Liu

    (Division of Biostatistics, Washington University in St. Louis, St. Louis, MO 63110, USA)

  • Qiong Yang

    (Department of Biostatistics, School of Public Health, Boston University, Boston, MA 02118, USA)

  • Juanjuan Fan

    (Department of Mathematics and Statistics, San Diego State University, San Diego, CA 92182, USA)

  • Richard A. Levine

    (Department of Mathematics and Statistics, San Diego State University, San Diego, CA 92182, USA)

Abstract

Greedy search (GS) or exhaustive search plays a crucial role in decision trees and their various extensions. We introduce an alternative splitting method called smooth sigmoid surrogate (SSS) in which the indicator threshold function used in GS is approximated by a smooth sigmoid function. This approach allows for parametric smoothing or regularization of the erratic and discrete GS process, making it more effective in identifying the true cutoff point, particularly in the presence of weak signals, as well as less prone to the inherent end-cut preference problem. Additionally, SSS provides a convenient means of evaluating the best split by referencing a parametric nonlinear model. Moreover, in many variants of recursive partitioning, SSS can be reformulated as a one-dimensional smooth optimization problem, rendering it computationally more efficient than GS. Extensive simulation studies and real data examples are provided to evaluate and demonstrate its effectiveness.

Suggested Citation

  • Xiaogang Su & George Ekow Quaye & Yishu Wei & Joseph Kang & Lei Liu & Qiong Yang & Juanjuan Fan & Richard A. Levine, 2024. "Smooth Sigmoid Surrogate (SSS): An Alternative to Greedy Search in Decision Trees," Mathematics, MDPI, vol. 12(20), pages 1-28, October.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:20:p:3190-:d:1496926
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    References listed on IDEAS

    as
    1. Torsten Hothorn & Achim Zeileis, 2008. "Generalized Maximally Selected Statistics," Biometrics, The International Biometric Society, vol. 64(4), pages 1263-1269, December.
    2. Jushan Bai, 1997. "Estimation Of A Change Point In Multiple Regression Models," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 551-563, November.
    3. Hothorn, Torsten & Lausen, Berthold, 2003. "On the exact distribution of maximally selected rank statistics," Computational Statistics & Data Analysis, Elsevier, vol. 43(2), pages 121-137, June.
    4. Shih, Yu-Shan & Tsai, Hsin-Wen, 2004. "Variable selection bias in regression trees with constant fits," Computational Statistics & Data Analysis, Elsevier, vol. 45(3), pages 595-607, April.
    5. Xiaogang Su & Karen Meneses & Patrick McNees & Wesley O. Johnson, 2011. "Interaction trees: exploring the differential effects of an intervention programme for breast cancer survivors," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 60(3), pages 457-474, May.
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