An Efficient Method for Extracting Fuzzy Classification Rules from High Dimensional Data
Stephen L. Chin
Rockwell Science Center, 1049 Camino Dos Rios Thousand Oaks, California 91360, USA
We present an efficient method for extracting fuzzy classification rules from high dimensional data. A cluster estimation method called subtractive clustering is used to efficiently extract rules from a high dimensional feature space. A complementary search method can quickly identify the important input features from the resultant high dimensional fuzzy classifier, and thus provides the ability to quickly generate a simpler, more robust fuzzy classifier that uses a minimal number of input features. These methods are illustrated through the benchmark iris data and through two aerospace applications.