Spectroscopy and Spectral Analysis, Volume. 34, Issue 3, 638(2014)
Genetic Algorithm Based Multi-Objective Least Square Support Vector Machine for Simultaneous Determination of Multiple Components by Near Infrared Spectroscopy
The near infrared (NIR) spectrum contains a global signature of composition, and enables to predict different properties of the material. In the present paper, a genetic algorithm and an adaptive modeling technique were applied to build a multi-objective least square support vector machine (MLS-SVM), which was intended to simultaneously determine the concentrations of multiple components by NIR spectroscopy. Both the benchmark corn dataset and self-made Forsythia suspense dataset were used to test the proposed approach. Results show that a genetic algorithm combined with adaptive modeling allows to efficiently search the LS-SVM hyperparameter space. For the corn data, the performance of multi-objective LS-SVM was significantly better than models built with PLS1 and PLS2 algorithms. As for the Forsythia suspense data, the performance of multi-objective LS-SVM was equivalent to PLS1 and PLS2 models. In both datasets, the over-fitting phenomena were observed on RBFNN models. The single objective LS-SVM and MLS-SVM didn’t show much difference, but the one-time modeling convenience allows the potential application of MLS-SVM to multicomponent NIR analysis.
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XU Bing, WANG Xing, Dhaene Tom, SHI Xin-yuan, Couckuyt Ivo, BAI Yan, QIAO Yan-jiang. Genetic Algorithm Based Multi-Objective Least Square Support Vector Machine for Simultaneous Determination of Multiple Components by Near Infrared Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2014, 34(3): 638
Received: Apr. 16, 2013
Accepted: --
Published Online: Mar. 14, 2014
The Author Email: Bing XU (btcm@163.com)