Kernel combination through genetic programming for image classification

Abstract

Support vector machine is a supervised learning technique which uses kernels to perform nonlinear separations of data. In this work, we propose a combination of kernels through genetic programming in which the individual fitness is obtained by a K-NN classifier using a kernel-based distance measure. Experiments have shown that our method KGP-K is much faster than other methods during training, but it is still able to generate individuals (i.e., kernels) with competitive performance (in terms of accuracy) to the ones that were produced by other methods. KGP-K produces reasonable kernels to use in the SVM with no knowledge about the distribution of data, even if they could be more complex than the ones generated by other methods and, therefore, they need more time during tests.