Rule Extraction via Dynamic Discretization with an Application to Air Quality Modelling

Association rule extraction is a very well-known and important problem in machine learning, and especially in the sub-field of explainable machine learning. Association rules are naturally extracted from data sets with Boolean (or at least categorical) attributes. In order for rule extraction algorithms to be applicable to data sets with numerical attributes as well, data must be suitably discretized, and a great amount of work has been devoted to finding good discretization algorithms, taking into account that optimal discretization is a NP-hard problem. Motivated by a specific application, in this paper we provide a novel discretization algorithm defined as an (heuristic) optimization problem and solved by an evolutionary algorithm, and we test its performances against well-known available solutions, proving (experimentally) that we are able to extract more rules in a easier way.