Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System
This work addresses the need for explainable AI by providing a method to tailor interpretable models for specific use cases, though it is incremental in its improvements over existing rule-based systems.
The authors tackled the challenge of creating interpretable machine learning models by proposing SupRB, a rule-based system that uses separate optimizers for rule discovery and composition, achieving performance comparable to XCSF while offering better control over model structure and reduced sensitivity to randomness.
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.