Including multi-objective abilities in the Hybrid Intelligent Suite for decision support

Hybrid intelligent systems (HIS) are very successful in tackling problems comprising of more than one distinct computational subtask. For instance, decision-making problems are good candidates for HIS because of their frequent dual nature. This is because supporting decision-making most often involves two phases: (i) forecasting decision scenarios and (ii) searching in those scenarios. In addition to reducing the inherent uncertainty and effort in decision making, previous works in the area of decision support have shown that some of the inconveniences of the dasiaInverse Problempsila can be overcome by the use of Hybrid Intelligent Decision Suites (HIDS). This paper extends HIDS by including a third module that deals with multi-objective (MO) tasks through Evolutionary Multi-Objective Optimization (EMOO). This EMOO module helps by creating the Pareto front for each forecast scenario produced by Artificial Neural Networks (ANN), acting here as the predictive engine of the decision support system. In order to interface better with decision makers, we use a fuzzy-heuristic module of the original HIDS. To test this concept we have applied our new approach to two distinct problems: (1) diagnosis of heart diseases (of the proben-1 data-set) and (2) automobile feature selection (of UCI data-set). Results have indicated that this new ensemble of intelligent techniques enhances the quality of decision making.