Reduction of input vectors by using of attribute selection in rule extraction method from trained neural networks

The rule extraction method, which extracts fuzzy if-then rules from trained neural networks, needs to input all combinations of antecedent fuzzy sets. For high-dimensional problems, it is very hard to input all combinations to neural networks because the number of such combinations is exponentially increased. Our attribute selection method can reduce the number of input combinations.