  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.
