Multi-objective genetic algorithm-based sample selection for partial least squares model building with applications to near-infrared spectroscopic data.

Hideyuki Shinzawa; Boyan Li; Takehiro Nakagawa; Katsuhiko Maruo; Yukihiro Ozaki

doi:10.1366/000370206777670576

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Multi-objective genetic algorithm-based sample selection for partial least squares model building with applications to near-infrared spectroscopic data.

Hideyuki Shinzawa·Boyan Li·Takehiro Nakagawa·Katsuhiko Maruo·Yukihiro Ozaki

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Abstract

In this study, multi-objective genetic algorithms (GAs) are introduced to partial least squares (PLS) model building. This method aims to improve the performance and robustness of the PLS model by removing samples with systematic errors, including outliers, from the original data. Multi-objective GA optimizes the combination of these samples to be removed. Training and validation sets were used to reduce the undesirable effects of over-fitting on the training set by multi-objective GA. The reduction of the over-fitting leads to accurate and robust PLS models. To clearly visualize the factors of the systematic errors, an index defined with the original PLS model and a specific Pareto-optimal solution is also introduced. This method is applied to three kinds of near-infrared (NIR) spectra to build PLS models. The results demonstrate that multi-objective GA significantly improves the performance of the PLS models. They also show that the sample selection by multi-objective GA enhances the ability of the PLS models to detect samples with systematic errors.

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Algorithms Biomimetics Computer Simulation Data Interpretation, Statistical Least-Squares Analysis Models, Chemical Models, Genetic Models, Statistical Spectrophotometry, Infrared Evaluation Study Journal Article

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Hideyuki Shinzawa, Boyan Li, Takehiro Nakagawa, Katsuhiko Maruo, Yukihiro Ozaki. 2006. Multi-objective genetic algorithm-based sample selection for partial least squares model building with applications to near-infrared spectroscopic data.. https://doi.org/10.1366/000370206777670576

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