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Interaction-Transformation Evolutionary Algorithm for Symbolic Regression.

F O de Franca1, G S I Aldeia2

  • 1Center for Mathematics, Computation and Cognition, Heuristics, Analysis and Learning Laboratory, Federal University of ABC, Santo Andre, Brazil folivetti@ufabc.edu.br.

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Summary
This summary is machine-generated.

A new method called Interaction-Transformation (IT) and its algorithm ITEA improve Symbolic Regression. ITEA offers competitive accuracy and automates feature importance extraction for better model interpretability.

Keywords:
Interaction–TransformationSymbolic Regressionevolutionary algorithms.

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Area of Science:

  • Machine Learning
  • Symbolic Regression
  • Data Mining

Background:

  • Symbolic Regression aims to find interpretable mathematical expressions.
  • Existing methods like SymTree using Interaction-Transformation (IT) show potential but struggle with scalability.
  • Problem dimension limits the effectiveness of prior IT-based algorithms.

Purpose of the Study:

  • Introduce a novel, mutation-only Evolutionary Algorithm (ITEA) for evolving IT expressions.
  • Enable users to control expression complexity by specifying the maximum number of terms.
  • Evaluate ITEA's performance against established regression models.

Main Methods:

  • Developed ITEA, a mutation-only Evolutionary Algorithm for Symbolic Regression.
  • Utilized the Interaction-Transformation (IT) representation for structured solutions.
  • Compared ITEA against linear, nonlinear, and other Symbolic Regression models.

Main Results:

  • ITEA achieves competitive or superior approximations compared to existing Symbolic Regression models.
  • ITEA demonstrates performance comparable to state-of-the-art nonlinear models.
  • The structured IT representation allows for automated extraction of original feature importance.

Conclusions:

  • ITEA offers a scalable and effective approach to Symbolic Regression using the IT representation.
  • ITEA provides an advantage in automating model explanation and feature importance analysis.
  • The method enhances interpretability and predictive accuracy in regression tasks.