SupRB: A Supervised Rule-based Learning System for Continuous Problems
This work addresses the need for interpretable machine learning in industrial settings like machinery parametrization, where trust relies on both prediction quality and understandable explanations, though it is incremental as it adapts existing LCS methods to continuous choices.
The authors tackled the problem of applying rule-based learning to continuous decision problems by introducing SupRB, a Pittsburgh-style learning classifier system that learns a quality function from examples and provides human-readable rules, achieving applicability on a simplified additive manufacturing model.
We propose the SupRB learning system, a new Pittsburgh-style learning classifier system (LCS) for supervised learning on multi-dimensional continuous decision problems. SupRB learns an approximation of a quality function from examples (consisting of situations, choices and associated qualities) and is then able to make an optimal choice as well as predict the quality of a choice in a given situation. One area of application for SupRB is parametrization of industrial machinery. In this field, acceptance of the recommendations of machine learning systems is highly reliant on operators' trust. While an essential and much-researched ingredient for that trust is prediction quality, it seems that this alone is not enough. At least as important is a human-understandable explanation of the reasoning behind a recommendation. While many state-of-the-art methods such as artificial neural networks fall short of this, LCSs such as SupRB provide human-readable rules that can be understood very easily. The prevalent LCSs are not directly applicable to this problem as they lack support for continuous choices. This paper lays the foundations for SupRB and shows its general applicability on a simplified model of an additive manufacturing problem.