论文标题
基于规则的进化贝叶斯学习
Rule-based Evolutionary Bayesian Learning
论文作者
论文摘要
在我们以前的工作中，我们介绍了基于规则的贝叶斯回归，这种方法利用了两个概念：（i）贝叶斯推断，进行一般框架和不确定性量化以及（ii）基于规则的系统，用于纳入专家知识和直觉。最终的方法造成了相当于普通贝叶斯先验的惩罚，但它还包括通常在标准贝叶斯语境中无法获得的信息。在这项工作中，我们使用语法进化扩展了上述方法，这是一种符号遗传编程技术，我们用于自动化规则的推导。我们的动机是，语法进化可以潜在地从数据中检测出有价值的信息，相当于专家知识的模式。我们通过将基于规则的进化贝叶斯学习技术应用于合成和真实数据来说明使用基于规则的贝叶斯学习技术，并根据点预测和相关的不确定性检查结果。
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.
