Kernel Construction and Feature Subset Selection in Support Vector Machines

摘要

Kernel functions have an important role in the performance of Support Vector Machines (SVMs), since they form the geometry of the feature space. Manual designing of kernel functions is an expensive task and requires domain-specific knowledge. In this article, we propose a new method to automatically construct kernel functions and select optimal subsets of features. We achieve this by combining primitive kernels and subsets of features using Genetic Programming (GP). Our experiments show that the proposed method drastically improves the prediction accuracy of SVMs.