Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection
<|reference_start|>Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection: 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) non-linear 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.<|reference_end|>
@article{gagné2006genetic,
    title={Genetic Programming for Kernel-based Learning with Co-evolving Subsets
  Selection},
    author={Christian Gagn'e (INRIA Futurs, ISI), Marc Schoenauer (INRIA Futurs,
  LRI), Mich`ele Sebag (LRI), Marco Tomassini (ISI)},
    journal={Dans PPSN'06, 4193 (2006) 1008-1017},
    year={2006},
    archivePrefix={arXiv},
    eprint={cs/0611135},
    primaryClass={cs.AI}
}