Rule-based Evolutionary Bayesian Learning
This work addresses the need for automated expert knowledge incorporation in Bayesian models, but it is incremental as it builds on prior rule-based Bayesian regression methods.
The paper tackles the problem of automating rule derivation in Bayesian regression by extending a rule-based Bayesian method with grammatical evolution, a symbolic genetic programming technique, to detect patterns from data that mimic expert knowledge, and applies it to synthetic and real data to evaluate point predictions and uncertainty.
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.