发表机构
Tohoku University; DIC Corporation; Tokyo Institute of Science; Kumamoto University; Sigma-i Co. Ltd.（东北大学; DIC株式会社; 东京理科大学; 熊本大学; Sigma-i有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究将糖含量估计的波长选择建模为二元黑箱优化问题，提出基于组合贝叶斯优化的方法，可提升偏最小二乘回归的预测精度与波长选择的鲁棒性。
AI 中文摘要
波长选择是近红外光谱中重要的预处理方法，用于提高光谱数据的预测精度和可解释性。本文将糖含量估计的波长区域选择问题建模为二元黑箱优化问题，并提出一种基于贝叶斯优化的方法。该方法构建稀疏二次代理模型，通过汤普森采样依次提取感兴趣的波长区域；将获取函数最小化问题转化为二次无约束二元优化问题，通过模拟退火或量子退火求解。实验表明，与基于遗传算法的选择方法和模拟退火相比，所提方法可提高偏最小二乘回归的预测精度，且得到的波长区域更一致；在1位局部扰动下，所选波长区域的验证集观测值与预测值的均方根误差波动极小，说明该方法收敛到更平滑的误差景观，可避免孤立的过拟合解。这些结果表明，组合贝叶斯优化是光谱预测任务中鲁棒特征选择的有效框架。
英文摘要
Wavelength selection is one of the important preprocessing methods in near-infrared spectroscopy to improve prediction accuracy and interpretability of spectral data. We formulate wavelength-region selection for sugar content estimation as a binary black-box optimization problem and propose a method based on Bayesian optimization. The proposed method constructs a sparse quadratic surrogate model and sequentially extracts interested wavelength regions by Thompson sampling. Minimizing an acquisition function is performed as a quadratic unconstrained binary optimization problem by simulated or quantum annealing. Experiments show that the proposed method improves the prediction accuracy of partial least squares regression and yields more consistent wavelength regions than genetic-algorithm-based selection and simulated annealing. Under one-bit local perturbations, the selected wavelength regions show minimal fluctuations in root mean square errors between observed and predicted values of a validation set. This local stability suggests that our method converges to a smoother error landscape and avoids isolated overfitted solutions. These results indicate that combinatorial Bayesian optimization is a useful framework for robust feature selection in spectroscopic prediction tasks.
