Multi-Kernel Partial Least Squares Regression based on Adaptive Genetic Algorithm

DOI

10.2991/amcce-15.2015.25How to use a DOI?

Keywords

Multi-kernel learning (MKL); Kernel partial least squares (KPLS); Adaptive genetic algorithm (AGA)

Abstract

Kernel learning has been a focus of machine learning domain recently. Kernel partial least squares (KPLS) algorithm can construct nonlinear model using extract latent variables from the input and output data space simultaneously. However, generalization performance of KPLS model relies mostly on kernel types and kernel parameters, which are difference to modeling of different applicable background. Intelligent optimization algorithm can be used to search these parameters. Thus, a new multi-kernel partial least squares regression approach based on linear multi-kernel construction method and adaptive genetic algorithm (AGA) is proposed in this paper. Normally used global and local kernels are weighed to obtain the mixed multi-kernel of KPLS algorithm. These kernel’s parameters and weighting coefficients are selected using AGA optimization algorithm. The experimental results based on Benchmark data set show that the proposed approach has better prediction performance than that of single kernel based modeling method.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shaowei Liu
AU  - Jian Tang
AU  - Dong Yan
PY  - 2015/04
DA  - 2015/04
TI  - Multi-Kernel Partial Least Squares Regression based on Adaptive Genetic Algorithm
BT  - Proceedings of the 2015 International Conference on Automation, Mechanical Control and Computational Engineering
PB  - Atlantis Press
SP  - 141
EP  - 144
SN  - 1951-6851
UR  - https://doi.org/10.2991/amcce-15.2015.25
DO  - 10.2991/amcce-15.2015.25
ID  - Liu2015/04
ER  -