A hybrid case base reasoning system for forecasting

CBR (Case-based reasoning) is an effective reasoning mechanism that solves a new problem by remembering a previous similar knowledge and by reusing information and knowledge of that features. The case-base is a set of small case-bases. Each small case-base can be viewed as the result of granular computing. To obtain an efficient CBR system, this paper proposed a hybrid CBR system by introducing feature selection and Granular computing, it also incorporate similarity margin concept and Gaussian kernel fuzzy rough sets in case-based organization.