2. Decision support system “ A ggregation and R anking m ethod ” (C-FAR m ), use an innovative approach to create composite indicators allowing ranking various items (countries, firms, consumers, etc.). C-FAR m is a helpful tool to aggregate multi-dimensional information and extract knowledge for decision-making in many areas.
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6. Three steps to aggregate complex information First, C-FAR m realizes self-organization of items into homogeneous subsets (clustering), through a learning process that takes into account the positive and negative interactions. Second , an appropriate weights vector is determined for each item. Finally , the weighting vectors are applied to original data to calculate the composite indicator and make the overall ranking.
7. Breakthrough C-FARm solves a major concern of aggregation problems whereby the question of the importance of each variable is still valid. The weighting system can be characterized as objective since it emanates from the informational content of the variables themselves and their internal dynamics. This last feature of C-FARm represents a valuable step forward and a going-beyond what is currently practiced in terms of classification / aggregation. These advances are based on benefits of Artificial Neural Networks model.
