Molecular Imaging and Photonics Unit, Department of Chemistry , Katholieke Universiteit Leuven , Celestijnenlaan 200F , B-3001 Leuven , Belgium.
Laboratoire de Spectrochimie Infrarouge et Raman - UMR 8516 , Université de Lille - Sciences et Technologies , Bâtiment C5 , 59655 Villeneuve d'Ascq , France.
Anal Chem. 2018 Sep 18;90(18):10738-10747. doi: 10.1021/acs.analchem.8b01270. Epub 2018 Sep 6.
An approach exploiting the principles of Receiver Operating Characteristic (ROC) curves for the simultaneous optimization of both the complexity and the decision threshold in Soft Independent Modeling of Class Analogy (SIMCA) classification models is here proposed. The outcomes resulting from the analysis of two simulated and four real case-studies highlight that, in the presence of strong overlap among various categories of samples, the implemented method can lead to better classification efficiency in external validation, compared to fixing such a threshold a priori. This guarantees a higher robustness toward class dispersion. On the other hand, in cases of clearer and more definite separation among the different groups of observations, their classification performance is equally satisfactory for test samples.
本文提出了一种利用接收器操作特性（ROC）曲线原理来同时优化软独立建模分类分析法（SIMCA）分类模型的复杂性和决策阈值的方法。通过对两个模拟案例和四个实际案例的分析，结果表明，在各种样本类别之间存在较强重叠的情况下，与预先固定该阈值相比，所采用的方法可以在外部验证中提高分类效率，从而具有更高的类分散稳健性。另一方面，在不同组观测值之间的分离更清晰、更明确的情况下，对于测试样本，它们的分类性能同样令人满意。
