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Fuzzy Classification Scheme Mapping For Decision Making

Classification schemes organize objects into hierarchy structure of knowledge by grouping them with common characteristics. Managers prefer resorting to existing classifications schemes for decision making for their advantages over perplexity and huge amount of detail bottom information. However, single classification scheme is not enough for certain purposes. More and more applications call for the need of information integration from heterogeneous sources. To facilitate the inter-operability and integration among various sources of information, we propose a fuzzy approach to realize the mapping across their backbones-classification schemes. The proposed approach defines the fuzzy matching degree by matching information in descriptor, feature and neighborhoods level. Preliminary results show that managers can easily analyze the relations across heterogeneous classification schemes by visualizing the mapping pattern in research area. An illustration of expert profiling is also given to validate the role of fuzzy classification scheme mapping in decision-making.