… , interpretability, and fairness. In nutshell, the essential objective of this study is to develop a
rule-based consensus model with the optimal allocation of … is designed by allocating certain …

… We propose a framework to derive interpretable decision rules with good performances for
… explainable decision rules for MDPs. We highlight the challenges of computing optimal tree …

… strongest advantages of belief rule-based (BRB) systems is good interpretability. Currently,
… only but overlook interpretability. In this study, the definition of interpretability in a BRB system …

… interpretable policy with an optimal or, possibly, near-optimal … the constraints of an interpretable
policy architecture, which … an interpretable scoring rule (specifically, linear scoring rules…

… In response to these problems, in this paper we apply a multi-objective genetic optimization
approach to design interpretability-oriented fuzzy rule-based classification systems from data…

… In this setting, a decision maker aims to optimize dynamic treatment rules for patients based
on their health status, health risks, predicted future health trajectories, and costs and benefits …

… to interpretable clustering, we propose a new tree-based machine learning algorithm, where
interpretability is taken … due to the importance of intuitive assignment rules in many settings. …

… Such issues of interpretability and explainability have seen … with an easily interpretable
optimization rule, that explains … trees to represent interpretable optimization rules, we propose …

… node represents a rule which guides to make decisions. Some researches combine the
tree structure with RL to improve the explainability. For example, a novel interpretable Q-learning …

… interpretability measures and techniques for obtaining more interpretable linguistic fuzzy
rule-based … versus semantic interpretability” considering the two main kinds of measures; and “…