Rule-based Evolutionary Bayesian Learning

TL;DR

This paper introduces an extension of rule-based Bayesian regression that uses grammatical evolution to automate rule derivation, enhancing the incorporation of data-driven patterns and expert knowledge for improved predictions.

Contribution

It combines grammatical evolution with rule-based Bayesian learning to automate rule creation, advancing the integration of data-driven insights and expert knowledge.

Findings

Effective on synthetic and real data

Improves point predictions and uncertainty quantification

Automates rule derivation using genetic programming

Abstract

In our previous work, we introduced the rule-based Bayesian Regression, a methodology that leverages two concepts: (i) Bayesian inference, for the general framework and uncertainty quantification and (ii) rule-based systems for the incorporation of expert knowledge and intuition. The resulting method creates a penalty equivalent to a common Bayesian prior, but it also includes information that typically would not be available within a standard Bayesian context. In this work, we extend the aforementioned methodology with grammatical evolution, a symbolic genetic programming technique that we utilise for automating the rules' derivation. Our motivation is that grammatical evolution can potentially detect patterns from the data with valuable information, equivalent to that of expert knowledge. We illustrate the use of the rule-based Evolutionary Bayesian learning technique by applying it to synthetic as well as real data, and examine the results in terms of point predictions and associated uncertainty.