Diversified Residual Symbolic Regression

TL;DR

The paper introduces Diversified Residual Symbolic Regression (DRSR), a method that finds multiple diverse mathematical expressions fitting data, aiding interpretability and domain knowledge integration.

Contribution

It proposes a novel approach that promotes diversity in residual patterns in symbolic regression, enabling better exploration of underlying relationships.

Findings

DRSR produces more diverse expressions than conventional SR on synthetic data.

On real-world astronomical data, DRSR discovers multiple expressions consistent with physical laws.

DRSR maintains high predictive accuracy while enhancing interpretability through diversity.

Abstract

Symbolic regression (SR) aims to discover explicit mathematical expressions that explain observed data and is widely used in domains where interpretability is essential. Because interpretability requires expressions to reflect meaningful regularities, SR is sensitive to observations that deviate from the dominant relationship. Such irregular observations, or outliers, are common in real-world data and can hinder SR from identifying underlying regularities. Robust regression mitigates this by downweighting observations with large residuals. However, deciding which observations should be treated as outliers is often ambiguous and depends on user interpretation and domain knowledge, a perspective largely overlooked in existing SR studies. This motivates approaches that present multiple candidate expressions, allowing users to examine different residual patterns and choose expressions consistent with their expertise. We propose diversified residual symbolic regression (DRSR), which achieves high predictive accuracy while promoting diversity with respect to residual patterns based on the Quality-Diversity paradigm. DRSR collects multiple expressions that fit the data well but differ in how residuals are distributed, enabling post-search selection aligned with domain knowledge. On a synthetic mixture dataset, DRSR produces more diverse expressions than conventional SR while capturing multiple underlying relationships. On a real-world astronomical dataset, DRSR discovers multiple expressions consistent with known physical relationships.