A Novel Memetic Strategy for Optimized Learning of Classification Trees
This addresses the need for interpretable machine learning models in domains requiring transparency, though it appears incremental compared to existing MILP-based exact formulations.
The authors tackled the problem of building interpretable classification trees by proposing a novel evolutionary algorithm with a memetic approach that handles datasets with thousands of points, achieving competitive generalization capabilities with state-of-the-art methods.
Given the increasing interest in interpretable machine learning, classification trees have again attracted the attention of the scientific community because of their glass-box structure. These models are usually built using greedy procedures, solving subproblems to find cuts in the feature space that minimize some impurity measures. In contrast to this standard greedy approach and to the recent advances in the definition of the learning problem through MILP-based exact formulations, in this paper we propose a novel evolutionary algorithm for the induction of classification trees that exploits a memetic approach that is able to handle datasets with thousands of points. Our procedure combines the exploration of the feasible space of solutions with local searches to obtain structures with generalization capabilities that are competitive with the state-of-the-art methods.