Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System

TL;DR

This paper introduces SupRB, a rule-based learning system that enhances interpretability and control by independently optimizing rule fitnesses, demonstrating comparable performance to XCSF with improved stability and explainability on regression tasks.

Contribution

SupRB's novel approach separates rule discovery and set composition optimizers, enabling tailored models and increased control over explainability and stability.

Findings

SupRB performs comparably to XCSF on regression problems.

SupRB shows less sensitivity to random seeds and data splits.

SupRB offers easier control of model structure and explainability.

Abstract

Achieving at least some level of explainability requires complex analyses for many machine learning systems, such as common black-box models. We recently proposed a new rule-based learning system, SupRB, to construct compact, interpretable and transparent models by utilizing separate optimizers for the model selection tasks concerning rule discovery and rule set composition.This allows users to specifically tailor their model structure to fulfil use-case specific explainability requirements. From an optimization perspective, this allows us to define clearer goals and we find that -- in contrast to many state of the art systems -- this allows us to keep rule fitnesses independent. In this paper we investigate this system's performance thoroughly on a set of regression problems and compare it against XCSF, a prominent rule-based learning system. We find the overall results of SupRB's evaluation comparable to XCSF's while allowing easier control of model structure and showing a substantially smaller sensitivity to random seeds and data splits. This increased control can aid in subsequently providing explanations for both training and final structure of the model.