An effective and efficient hierarchical fuzzy rule based classifier

In this paper, we propose a hierarchical fuzzy rule based classifier (HFRBC) for the classification problem with large number of classes and continuous attributes. A hierarchical clustering concept is introduced to achieve a finer fuzzy partition. Critical attributes are used to perform the cluster splitting and generate a cluster splitting tree. The effective attributes for the terminal clusters in the cluster splitting tree are picked so as to reduce the size of the fuzzy-rule set and hence reduce the computational complexity. The fuzzy rule generation procedures and classification procedures of the proposed HFRBC are simple and easily implemented. We have successfully applied the HFRBC to the classification problem of the working wafers in an ion implanter.