Genetically Constructed Kernels for Support Vector Machines

Abstract

Data mining for customer relationship management involves the task of binary classification, e.g. to distinguish between customers who are likely to respond to direct mail and those who are not. The support vector machine (SVM) is a powerful learning technique for this kind of problem. To obtain good classification results the selection of an appropriate kernel function is crucial for SVM. Recently, the evolutionary construction of kernels by means of meta-heuristics has been proposed to automate model selection. In this paper we consider genetic algorithms (GA) to generate SVM kernels in a data driven manner and investigate the potential of such hybrid algorithms with regard to classification accuracy, generalisation ability of the resulting classifier and computational efficiency. We contribute to the literature by: (1) extending current approaches for evolutionary constructed kernels; (2) investigating their adequacy in a real world business scenario; (3) considering runtime issues together with measures of classification effectiveness in a mutual framework.