ECSEL: Explainable Classification via Signomial Equation Learning

TL;DR

ECSEL is an explainable classification method that learns signomial equations as compact, interpretable models, achieving high accuracy and revealing dataset insights efficiently.

Contribution

ECSEL introduces a novel approach to learn signomial equations for classification, combining interpretability with competitive accuracy and efficiency.

Findings

ECSEL recovers more target equations than state-of-the-art methods on benchmarks.

ECSEL achieves classification accuracy comparable to traditional machine learning models.

ECSEL exposes dataset biases and supports counterfactual reasoning in real-world cases.

Abstract

We introduce ECSEL, an explainable classification method that learns formal expressions in the form of signomial equations, motivated by the observation that many symbolic regression benchmarks admit compact signomial structure. ECSEL directly constructs a structural, closed-form expression that serves as both a classifier and an explanation. On standard symbolic regression benchmarks, our method recovers a larger fraction of target equations than competing state-of-the-art approaches while requiring substantially less computation. Leveraging this efficiency, ECSEL achieves classification accuracy competitive with established machine learning models without sacrificing interpretability. Further, we show that ECSEL satisfies some desirable properties regarding global feature behavior, decision-boundary analysis, and local feature attributions. Experiments on benchmark datasets and two real-world case studies i.e., e-commerce and fraud detection, demonstrate that the learned equations expose dataset biases, support counterfactual reasoning, and yield actionable insights.