Optimizing the soft independent modeling of class analogy (SIMCA) using statistical prediction regions.

The ultimate goal of a one-class classifier like the "rigorous" soft independent modeling of class analogy (SIMCA) is to predict with a certain confidence probability, the conformity of future objects with a given reference class. However, the SIMCA model, as currently implemented often suffers from an undercoverage problem, meaning that its observed sensitivity often falls far below the desired theoretical confidence probability, hence undermining its intended use as a predictive tool. To overcome the issue, the most reported strategy in the literature, involves incrementing the nominal confidence probability until the desired sensitivity is obtained in cross-validation.

Interpretable One-Class Classification of Raman Spectra Using Prediction Bands Estimated by Wavelet Regression.

Previously, we introduced a novel one-class classification (OCC) concept for spectra. It uses as acceptance space for genuine spectra of the target chemical, a prediction band in the wavelengths' space. As a decision rule, test spectra falling substantially outside this band are rejected as noncomplying with the target, and their deviations are documented in the wavelengths' space.