An improved rule generation method for evidence-based classification systems

摘要

A new method is described for generating rules which attempt to optimize classification when class samples are not contiguous nor necessarily segregated in feature space. The method combines well-known clustering techniques (Leader and K-Means methods) with Stochastic Relaxation to minimize a combined cluster entropy function. Further, a technique is developed which is capable of determining the cluster weights which optimize classification performance and reflect the Boolean structures of the associated convex clusters.