Dominance-based Rough Set Approach, basic ideas and main trends
This is an incremental review paper summarizing an existing methodology for decision-makers and researchers in MCDA and data mining.
The paper reviews the Dominance-based Rough Set Approach (DRSA), a methodology for Multiple Criteria Decision Aiding (MCDA) that handles monotonic and non-monotonic data by using simple preference information to provide explainable recommendations, highlighting its development and applications over time.
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 Słowiński.