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

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.

Separating Rule Discovery and Global Solution Composition in a Learning Classifier System

The utilization of digital agents to support crucial decision making is increasing in many industrial scenarios. However, trust in suggestions made by these agents is hard to achieve, though essential for profiting from their application, resulting in a need for explanations for both the decision making process as well as the model itself. For many systems, such as common deep learning black-box models, achieving at least some explainability requires complex post-processing, while other systems profit from being, to a reasonable extent, inherently interpretable. In this paper we propose an easily interpretable rule-based learning system specifically designed and thus especially suited for these scenarios and compare it on a set of regression problems against XCSF, a prominent rule-based learning system with a long research history. One key advantage of our system is that the rules' conditions and which rules compose a solution to the problem are evolved separately. We utilise independent rule fitnesses which allows users to specifically tailor their model structure to fit the given requirements for explainability. We find that the results of SupRB2's evaluation are 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 aids in subsequently providing explanations for both the training and the final structure of the model.