Automatic SVM Kernel Function Construction Based on Gene Expression Programming

Traditional Support Vector Machine needs pre-assumed kernel functions. This paper proposes a method via Gene Expression Programming to automatically construct the kernel. The contributions of this paper include: (1) proposing the concepts of GEP kernel and kernel tree; (2) proposing the properties of GEP kernel and the kernel relation theorem; (3) proposing GEP based Support Vector Machine (KGEP-SVM), (4) decoding Kernel Individual Algorithm (DKIA) and Kernel Operators Operating Algorithm (KOOA), and (5) extensive experiments show that the average accuracy of the method is increased by 4% and generation of GEP Kernel is about 150.