Simplification is a popular trend in most activities and areas, including computer science and machine learning. It supports a deeper understanding of application domains and helps with generalisation while reducing computational and storage costs. The paper presents research in which a simplifying procedure in the form of discretisation was applied to the input data and patterns discovered by the Dominance-Based Rough Set Approach. The influence of transformations was studied from the perspective of attribute importance estimated by rankings and performance of rule-based classifiers simplified by ranking-driven rule filtering. The experimental results show many cases of improved predictions and enhanced interpretability due to the employed processing.