Rough set and rule-based multicriteria decision aiding

The aim of multicriteria decision aiding is to give the decision maker a recommendation concerning a set of objects evaluated from multiple points of view called criteria. Since a rational decision maker acts with respect to his/her value system, in order to recommend the most-preferred decision, one must identify decision maker's preferences. In this paper, we focus on preference discovery from data concerning some past decisions of the decision maker. We consider the preference model in the form of a set of "if..., then..." decision rules discovered from the data by inductive learning. To structure the data prior to induction of rules, we use the Dominance-based Rough Set Approach (DRSA). DRSA is a methodology for reasoning about data, which handles ordinal evaluations of objects on considered criteria and monotonic relationships between these evaluations and the decision. We review applications of DRSA to a large variety of multicriteria decision problems

Incorporating stakeholders’ knowledge in group decision-making

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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

Dominance-based rough set approach and analytic network process for assessing urban transformation scenarios

For half a century, the significant development of intensive farming has led to a massive use of products such as pesticides. The excessive use of these substances has contaminated surface water and groundwater. Drinking water extraction points have also had to be abandoned. Some thirty years ago, in the southwest of France, a group of farmers decided to improve their farming methods, as well as developing new Best Environmental Practices, such as grass strips along streams and riparian forests. By combining the use of ELECTRE TRI-C multi-criteria model with a GIS, we were able to characterise the contribution of each farming area to the risk of surface water contamination with pesticides. We also assessed the effectiveness of different environmental practices. We found that the use of Best Environmental Practices led to a reduction in the risk of pesticides transfer. This methodology re-enforces decision support tools for water resource managers and agricultural and environmental stakeholders

Applying Q-Methodology to Investigate People’ Preferences for Multivariate Stimuli

This article serves as a step-by-step guide of a new application of Q-methodology to investigate people’s preferences for multivariate stimuli. Q-methodology has been widely applied in fields such as sociology, education and political sciences but, despite its numerous advantages, it has not yet gained much attention from experimental psychologists. This may be due to the fact that psychologists examining preferences, often adopt stimuli resulting from a combination of characteristics from multiple variables, and in repeated measure designs. At present, Q methodology has not been adapted to accommodate. We therefore developed a novel analysis procedure allowing Q-methodology to handle these conditions. We propose a protocol requiring five analyses of a decision process to estimate: (1) the preference of stimuli, (2) the dominance of variables, (3) the individual differences, (4) the interaction between individual differences and preference, and (5) the interaction between individual differences and dominance. The guide comes with a script developed in R (R Core Team, 2020) to run the five analyses; furthermore, we provide a case study with a detailed description of the procedure and corresponding results. This guide is particularly beneficial to conduct and analyze experiments in any research on people’s preferences, such as experimental aesthetics, prototype testing, visual perception (e.g., judgments of similarity/dissimilarity to a model), etc

Rough Set Applied to Air Pollution: A New Approach to Manage Pollutions in High Risk Rate Industrial Areas

This study presents a rough set application, using together the ideas of classical rough set approach, based on the indiscernibility relation and the dominance-based rough set approach (DRSA), to air micro-pollution management in an industrial site with a high environmental risk rate, such as the industrial area of Syracuse, located in the South of Italy (Sicily). This new data analysis tool has been applied to different decision problems in various fields with considerable success, since it is able to deal both with quantitative and with qualitative data and the results are expressed in terms of decision rules understandable by the decision-maker. In this chapter, some issue related to multi-attribute sorting (i.e. preference-ordered classification) of air pollution risk is presented, considering some meteorological variables, both qualitative and quantitative as attributes, and criteria describing the different objects (pollution occurrences) to be classified, that is, different levels of sulfur oxides (SOx), nitrogen oxides (NOx), and methane (CH4) as pollution indicators. The most significant results obtained from this particular application are presented and discussed: examples of ‘if, … then’ decision rules, attribute relevance as output of the data analysis also in terms of exchangeable or indispensable attributes/criteria, of qualitative substitution effect and interaction between them