A New Splitting Criterion for Better Interpretable Trees

A new splitting criterion for classification trees that generates better decision rules in terms of interpretability is proposed in this paper. The criterion is designed to find homogeneous rules that describe a significant number of instances with a short length. The proposed criterion considers only one side of a split to generate highly homogeneous rules and concurrently utilizes a function of sample ratios with an adjustable hyperparameter to control the coverage of rules. The distinctive feature of the proposed method is that it is applied adaptively at every split. We also introduce an efficient heuristic algorithm to determine an appropriate hyperparameter value for every split. Experimental results evaluated over 17 benchmark datasets show that the proposed criterion combined with the proposed heuristic constructs a better interpretable decision tree. It is verified through quantitative and qualitative analysis that the constructed tree produces highly interpretable rules, and its predictive performance is comparable to that of other popular criteria.