Metadata about: The Dominance-based Rough Set Approach (DRSA) is an innovative preference learning approach. It takes as input a set of objects (learning set) described with respect to a collection of condition and decision attributes. It generates a set of if-then decision rules. Initial versions of dominance based rough set approximation methods assume a single decision maker. Furthermore, the proposed extensions to group decision making mainly use an input oriented aggregation strategy, which requires a high level of agreement between the decision makers. In this paper, we propose an output oriented aggregation strategy to coherently combine different sets of decision rules obtained from different decision makers. The proposed aggregation algorithm is illustrated by using real-world data relative to a business school admission where two decision makers are involved. Results show that aggregation algorithm is able to reproduce the individual assignments of students with a very limited preferential information loss.

About: The Dominance-based Rough Set Approach (DRSA) is an innovative preference learning approach. It takes as input a set of objects (learning set) described with respect to a collection of condition and decision attributes. It generates a set of if-then decision rules. Initial versions of dominance based rough set approximation methods assume a single decision maker. Furthermore, the proposed extensions to group decision making mainly use an input oriented aggregation strategy, which requires a high level of agreement between the decision makers. In this paper, we propose an output oriented aggregation strategy to coherently combine different sets of decision rules obtained from different decision makers. The proposed aggregation algorithm is illustrated by using real-world data relative to a business school admission where two decision makers are involved. Results show that aggregation algorithm is able to reproduce the individual assignments of students with a very limited preferential information loss.

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