Dominance-based Rough Set Approach, basic ideas and main trends

TL;DR

The paper reviews the Dominance-based Rough Set Approach (DRSA), highlighting its principles, developments, and applications in decision making and data analysis, emphasizing its interpretability and evolution over time.

Contribution

It provides a comprehensive overview of DRSA's basic ideas, main trends, historical background, and software tools, emphasizing its significance in MCDA and data mining.

Findings

DRSA effectively handles multiple criteria decision problems.

It offers understandable and explainable recommendations.

The methodology has been extended beyond MCDA to general data analysis.

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.