An Efficient Neuro-Fuzzy-Genetic Data Mining Framework Based on Computational Intelligence

In this paper, we combine computational intelligence tools: neural network, fuzzy logic, and genetic algorithm to develop a data mining framework (DMFBCI), which discovers patterns and represents them in understandable forms. In the DMFBCI, input data are preprocessed by fuzzification or one-of-m coding, then, principal component analysis (PCA) is applied to reduce the dimensions of the preprocessed input variables in finding combinations of variables. The reduced dimensions of input variables are then used to train a radial basis probabilistic neural network (RBPNN) to classify the dataset according to the classes considered. A rule extraction technique is then applied in order to extract explicit knowledge from the trained neural networks and represent it in the form of fuzzy if-then rules. In the final stage, a genetic algorithm is used as a rule-pruning module to eliminate those weak rules that are still in the rule bases. Comparison with some known neural network classifier, the architecture we proposed has fast learning speed, and it is characterized by the incorporation of the possibility information into the consequents of classification rules in human understandable forms. The experiments show that the DMFBCI is more efficient and more robust than traditional method such as decision tree approaches such as CART, C4.5.