Dominance-based Rough Set Approach, basic ideas and main trends
Abstract
Dominance-based Rough Approach (DRSA) has been proposed as a machine learning and knowledge discovery methodology to handle Multiple Criteria Decision Aiding (MCDA). Due to its capacity of asking the decision maker (DM) for simple preference information and supplying easily understandable and explainable recommendations, DRSA gained much interest during the years and it is now one of the most appreciated MCDA approaches. In fact, it has been applied also beyond MCDA domain, as a general knowledge discovery and data mining methodology for the analysis of monotonic (and also non-monotonic) data. In this contribution, we recall the basic principles and the main concepts of DRSA, with a general overview of its developments and software. We present also a historical reconstruction of the genesis of the methodology, with a specific focus on the contribution of Roman S{\l}owi\'nski.
Keywords
Cite
@article{arxiv.2210.03233,
title = {Dominance-based Rough Set Approach, basic ideas and main trends},
author = {Jerzy Błaszczyński and Salvatore Greco and Benedetto Matarazzo and Marcin Szeląg},
journal= {arXiv preprint arXiv:2210.03233},
year = {2022}
}
Comments
This research was partially supported by TAILOR, a project funded by European Union (EU) Horizon 2020 research and innovation programme under GA No 952215. This submission is a preprint of a book chapter accepted by Springer, with very few minor differences of just technical nature