A methodology to support group multicriteria classification
Salem Chakhar, Inès Saad · 2011
The paper proposes a two-phase methodology to support groups in multicriteria classification problems. The first phase, which relies on dominance-based rough set approach, takes as input a set of assignment examples, and generates as output a set of collective decision rules representing a generalized description of the preference information of the decision makers. The second phase then applies these collective decision rules to classify all the decision objects. The methodology uses “if · · · then · · · ” aggregation rules that coherently implement the majority principle and veto effect and hence permit to obtain consensual decisions. Further more, the contribution of each decision maker in the collective decision is objectively measured by the quality of individual classification conduced by this decision maker during the first phase of the methodology. The methodology has been validated through the development of a prototype and applied to a nuclear risk management decision problem.