Genetic programming based pattern classification with feature space partitioning

Abstract

Genetic programming (GP) is an evolutionary technique and is gaining attention for its ability to learn the underlying data relationships and express them in a mathematical manner. Although GP uses the same principles as genetic algorithms, it is a symbolic approach to program induction; i.e., it involves the discovery of a highly fit computer program from the space of computer programs that produces a desired output when presented with a particular input. We have successfully applied the GP paradigm for the n-category pattern classification problem. The ability of the GP classifier to learn the data distributions depends upon the number of classes and the spatial spread of data. As the number of classes increases, it increases the difficulty for the GP classifier to resolve between classes. So, there is a need to partition the feature space and identify sub-spaces with reduced number of classes. The basic objective is to divide the feature space into sub-spaces and hence the data set that contains representative samples of n classes into sub-data sets corresponding to the sub-spaces of the feature space, so that some of the sub-data sets/spaces can have data belonging to only p-classes (p<n). The GP classifier is then evolved independently for the sub-data sets/spaces of the feature space. The GP classifier becomes simpler for some of the sub-data sets/spaces as only p classes are present. It also results in localized learning as the GP classifier has to learn the data distribution in only a sub-space of the feature space rather than in the entire feature space. In this paper, we are integrating the GP classifier with feature space partitioning (FSP) for localized learning to improve pattern classification.