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.

翻译：机器学习领域的不断进步催生了众多应用，这些应用通过精确预测有效解决了广泛问题。然而，在某些情况下，仅凭准确性可能并不足够。许多实际问题还需要对预测结果做出解释和可解释性。其中一种最受欢迎的可解释模型是分类规则。本文旨在提出一种基于MaxSAT的增量模型，用于学习可解释且平衡的规则，称为IMLIB。该新模型基于另外两种方法，一种基于SAT，另一种基于MaxSAT。基于SAT的方法限制了每条生成规则的大小，从而使其能够实现平衡。我们认为，与大小规则混合相比，这样的一组规则似乎更自然易懂。基于MaxSAT的方法称为IMLI，它提出了一种通过增量方式在数据集上应用模型来学习一组规则的性能提升技术。最后，使用多种数据库对IMLIB与IMLI进行了比较。IMLIB在准确性方面取得了与IMLI相当的结果，同时生成了尺寸更小且更平衡的规则。