2 research outputs found
Dominance-based Rough Set Approach, basic ideas and main trends
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.Comment: 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 natur