Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection

Abstract. Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed optimization problem; ii) nonlinear learning can be brought into linear learning thanks to the kernel trick and the mapping of the initial search space onto a high dimensional feature space. The kernel is designed by the ML expert and it governs the efficiency of the SVM approach. In this paper, a new approach for the automatic design of kernels by Genetic Programming, called the Evolutionary Kernel Machine (EKM), is presented. EKM combines a well-founded fitness function inspired from the margin criterion, and a co-evolution framework ensuring the computational scalability of the approach. Empirical validation on standard ML benchmark demonstrates that EKM is competitive using state-of-the-art SVMs with tuned hyper-parameters.

• Type: Journal
• Year: 2006
• Where: CORR
• Authors: Christian Gagné, Marc Schoenauer, Michèle Sebag, Marco Tomassini
• Keywords: CORR 2006, Education, Evolutionary Kernel Machine, Support Vector Machines, Well-posed Optimization Problem