Rough Sets in Machine Learning: A Review

This chapter emphasizes on the role played by rough set theory (RST) within the broad field of Machine Learning (ML). As a sound data analysis and knowledge discovery paradigm, RST has much to offer to the ML community. We surveyed the existing literature and reported on the most relevant RST theoretical developments and applications in this area. The review starts with RST in the context of data preprocessing (discretization, feature selection, instance selection and meta-learning) as well as the generation of both descriptive and predictive knowledge via decision rule induction, association rule mining and clustering. Afterward, we examined several special ML scenarios in which RST has been recently introduced, such as imbalanced classification, multi-label classification, dynamic/incremental learning, Big Data analysis and cost-sensitive learning.