Feature Mapping Through Maximization of the Atomic Interclass Distances

Abstract

We discuss a way of implementing feature mapping for classification problems by expressing the given data through a set of functions comprising of a mixture of convex functions. In this way, a certain pattern’s potential of belonging to a certain class is mapped in a way that promotes interclass separation, data visualization and understanding of the problem’s mechanics. In terms of enhancing separation, the algorithm can be used in two ways: to construct problem features to feed a classification algorithm or to detect a subset of problem attributes that could be safely ignored. In terms of problem understanding, the algorithm can be used for constructing a low dimensional feature mapping in order to make problem visualization possible. The whole approach is based on the derivation of an optimization objective which is solved with a genetic algorithm. The algorithm was tested under various datasets and it is successful in providing improved evaluation results. Specifically for Wisconsin breast cancer problem, the algorithm has a generalization success rate of 98% while for Pima Indian diabetes it provides a generalization success rate of 82%.