부산대학교 도서관

The design of interpretable classifiers is a major goal in machine learning since many applications rely on a complete understanding of the learning model decisions. Among all interpretable models available in the literature, Rough sets based models have become popular given the capability of rough sets to model imprecise data. Despite its success, some of the most used rough sets models are designed to work for categorical input data. Since this design choice may severely limit the application of such models in real world problems, in this paper, we aim at showing some strategies to enable rough sets based models to work with numerical datasets.