Genetically designed multiple-kernels for improving the SVM performance
Laura Silvia Dioşan, Mihai Oltean, Alexandrina Rogozan, Jean Pierre Pecuchet · 2007
Classical kernel-based classifiers only use a single kernel, butthe real world applications have emphasized the need to con-sider a combination of kernels also known as a multiple kernel in order to boost the performance. Our purpose isto automatically find the mathematical expression of a multiple kernel by evolutionary means. In order to achieve this purpose we propose a hybrid model that combines a Genetic Programming (GP) algorithm and a kernel-based Support Vector Machine (SVM) classifier. Each GP chromosome isa tree encoding the mathematical expression of a multiple kernel. Numerical experiments show that the SVM embedding the evolved multiple kernel performs better than the standard kernels for the considered classification problems.