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Improving Featured-based Soft Sensing through Feature Selection

机译：通过特征选择改善基于特色的软感应

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Driven by the expanding applications of spectroscopic technologies, many advancements have been reported for soft sensor modeling, which infers a sample’s properties from its spectroscopic readings. Because the number of wavelengths contained in a sample spectrum is usually much larger than the number of samples, “curse-of-dimensionality” is a common challenge that would affect the predictive power of the soft sensor. This challenge could be alleviated through variable selection. However, there is no guarantee that the truly relevant variables would be selected, and the selected variables are often (very) sensitive to the choice of training and validation data. To help address this challenge, we have developed a feature-based soft sensing approach by adapting the statistics pattern analysis (SPA) framework. In the SPA feature-based soft sensing, the features extracted from different segments of the complete spectrum were utilized to build the model. In this way, the information contained in the whole spectrum is used to build the model, while the number of the variables is significantly reduced. In this work, by integrating a variable selection approach we developed recently with SPA, we not only further improve the soft sensor’s prediction performance, but also identify the key underlying chemical information from spectroscopic data. The performance of the improved feature-based soft sensing approach, termed SPA-CEEVS, is demonstrated using two NIR datasets, and compared with several existing soft sensing approaches.
机译：通过光谱技术的扩展应用驱动，据报道了软传感器建模的许多进步，其从其光谱读数中递送了样本的性质。因为样品谱中包含的波长数通常大于样品的数量，所以“诅咒维度”是一种常见的挑战，这将影响软传感器的预测力。可以通过可变选择来缓解此挑战。但是，无法保证将选择真正相关的变量，并且所选变量通常对培训和验证数据的选择敏感。为了帮助解决这一挑战，我们通过调整统计模式分析（SPA）框架来开发了一种基于功能的软感测方法。在基于SPA特征的软感测中，利用了从完整频谱的不同段中提取的特征来构建模型。以这种方式，整个频谱中包含的信息用于构建模型，而变量的数量显着降低。在这项工作中，通过集成了我们最近开发的可变选择方法，我们不仅进一步提高了软传感器的预测性能，还可以从光谱数据中识别键底层化学信息。使用两个NIR数据集来证明改进的基于特征的软感测方法称为SPA-CEEV的性能，并与几种现有的软感测方法进行比较。

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