Assessment of Conformal Prediction and Standard Normal Distribution for Autonomous Consensus One-Class Classification

Determining if target samples are members of a particular source class of samples has a large variety of applications within many disciplines. In particular, one-class classification (OCC) is essential in many areas, such as food contamination or product authentication. There are numerous widely accepted methods for OCC, but these OCC methods involve optimizing tuning parameters such as the number of principal components (PCs). This study presents the development and application of a rigorous autonomous OCC process based on a hybrid fusion consensus technique, termed consensus OCC (Con OCC). The Con OCC method uses the new physicochemical responsive integrated similarity measure (PRISM) composed of multiple similarity measures all independent of optimization. Similarity values are fused to a single value describing the degree of sample similarity to a collection of samples. Two approaches are developed to translate each sample-wise PRISM value to a probability of class membership: conformal prediction p-values and z-scores. These two methods are evaluated as separate Con OCC processes using seven datasets measured across a variety of instruments. In both cases, class membership labels are not used to set decision thresholds, and classifiers are not optimized relative to respective tuning parameters. Results indicate that z-scoring often produces better results, but conformal prediction provides greater consistency across datasets. That is, z-score values tend to range across datasets while conformal prediction p-values do not.