Learning Discretized Bayesian Networks with GOMEA

arXiv:2402.12175v1 Announce Type: new
Abstract: Bayesian networks model relationships between random variables under uncertainty and can be used to predict the likelihood of events and outcomes while incorporating observed evidence. From an eXplainable AI (XAI) perspective, such models are interesting as they tend to be compact. Moreover, captured relations can be directly inspected by domain experts. In practice, data is often real-valued. Unless assumptions of normality can be made, discretization is often required. The optimal discretization, however, depends on the …

abstract arxiv bayesian cs.lg cs.ne events evidence explainable ai likelihood networks perspective random relations relationships type uncertainty variables xai