Learning Customised Decision Trees for Domain-knowledge Constraints.

•We propose a tree representation based on constrained matrices that leads to two new generic linear programming formulations of the optimal decision tree problem allowing to easily integrate domain-knowledge constraints.•Both formulations give the possibility for users to easily control complexity through the constraints on the number of leaf nodes and the maximum depth of the decision tree.•We theoretically show that elements of these matrices give the ability to easily formalise domain knowledge as constraints to improve trustworthiness.