An Incremental MaxSAT-based Model to Learn Interpretable and Balanced Classification Rules

TL;DR

The work tackles the need for interpretable decisions in binary classification by learning rule ensembles. It proposes IMLIB, an incremental MaxSAT-based method that combines SQFSAT’s constraint on rule size with IMLI’s partitioned, incremental training, plus a redundancy-reduction step to shrink rule size. Empirical evaluation on ten UCI datasets shows that IMLIB achieves equal or higher accuracy than IMLI while producing smaller, more balanced rule sets, albeit with longer training times. The results demonstrate improved interpretability through uniform rule sizes and compactness, offering a practical path toward explainable rule-based classification.

Abstract

The increasing advancements in the field of machine learning have led to the development of numerous applications that effectively address a wide range of problems with accurate predictions. However, in certain cases, accuracy alone may not be sufficient. Many real-world problems also demand explanations and interpretability behind the predictions. One of the most popular interpretable models that are classification rules. This work aims to propose an incremental model for learning interpretable and balanced rules based on MaxSAT, called IMLIB. This new model was based on two other approaches, one based on SAT and the other on MaxSAT. The one based on SAT limits the size of each generated rule, making it possible to balance them. We suggest that such a set of rules seem more natural to be understood compared to a mixture of large and small rules. The approach based on MaxSAT, called IMLI, presents a technique to increase performance that involves learning a set of rules by incrementally applying the model in a dataset. Finally, IMLIB and IMLI are compared using diverse databases. IMLIB obtained results comparable to IMLI in terms of accuracy, generating more balanced rules with smaller sizes.