A Deterministic and Symbolic Regression Hybrid Applied to Resting-State fMRI Data

  • Ilknur Icke
  • Nicholas A. Allgaier
  • Christopher M. Danforth
  • Robert A. Whelan
  • Hugh P. Garavan
  • Joshua C. Bongard
Chapter

Abstract

Symbolic regression (SR) is one the most popular applications of genetic programming (GP) and an attractive alternative to the standard deterministic regression approaches due to its flexibility in generating free-form mathematical models from observed data without any domain knowledge. However, GP suffers from various issues hindering the applicability of the technique to real-life problems. In this paper, we show that a hybrid deterministic regression (DR)/genetic programming based symbolic regression (GP-SR) algorithm outperforms GP-SR alone on a brain imaging dataset.

Keywords

Symbolic regression Hybrid algorithm Regularization Resting-state fMRI 

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Ilknur Icke
    • 1
  • Nicholas A. Allgaier
    • 2
  • Christopher M. Danforth
    • 2
  • Robert A. Whelan
    • 3
  • Hugh P. Garavan
    • 3
  • Joshua C. Bongard
    • 1
  1. 1.Department of Computer ScienceUniversity of Vermont & Vermont Complex Systems CenterBurlingtonUSA
  2. 2.Department of Mathematics and StatisticsUniversity of Vermont & Vermont Complex Systems CenterBurlingtonUSA
  3. 3.Department of PsychiatryUniversity of VermontBurlingtonUSA

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