Intelligent Multicriteria Decision Support System for a Periodic Prediction

This paper proposes an intelligent decision support system for the Incremental Periodic Prediction of the decision class to which an action is likely to belong. This method is based on three phases. The first consists of three steps: the construction of a family of criteria for the characterization of actions; the construction of a representative learning set for each of the decision classes; and the construction of a decision table. The second phase is based on the DRSA-Incremental algorithm that we propose for the inference and the updating of the set of decision rules following the sequential increment of the learning set. The third phase is meant to classify the “Potential Actions” in one of the predefined decision classes using the set of inferred decision rules. Our method is based on the DRSA (Dominance-based Rough Set Approach) which is a supervised learning technique permitting to extract the preferences of decision makers for the actions categorization. Applied in the context of MOOCs (Massive Open Online Courses) for the categorization of learners’ profiles, our approach proved a satisfactory classification quality that reaches 0.66.