Automated Knowledge Acquisition from Discrete Data Based on NEWFM

A useful technique for automated knowledge acquisition from a database is to select the minimum number of input features with the highest performance result. This paper presents automated knowledge acquisition to using a feature selection based on a neural network with weighted fuzzy membership functions (NEWFM). NEWFM supports the power and usefulness of fuzzy classification rules based on a non-overlap area measurement method. The non-overlap area measurement method selects the minimum number of input features with the highest performance result from initial input features by removing the worst input features one by one. The highest performance results in a non-overlap area distribution measurement method from Credit approval and Australian credit approval at the UCI repository are 87.75% and 87.10%, respectively.