Search for: All records

ABSTRACT 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 predictionp‐values andz‐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 thatz‐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 predictionp‐values do not. 

Modern developments in autonomous chemometric machine learning technology strive to relinquish the need for human intervention. However, such algorithms developed and used in chemometric multivariate calibration and classification applications exclude crucial expert insight when difficult and safety-critical analysis situations arise, e.g., spectral-based medical decisions such as noninvasively determining if a biopsy is cancerous. The prediction accuracy and interpolation capabilities of autonomous methods for new samples depend on the quality and scope of their training (calibration) data. Specifically, analysis patterns within target data not captured by the training data will produce undesirable outcomes. Alternatively, using an immersive analytic approach allows insertion of human expert judgment at key machine learning algorithm junctures forming a sensemaking process performed in cooperation with a computer. The capacity of immersive virtual reality (IVR) environments to render human comprehensible three-dimensional space simulating real-world encounters, suggests its suitability as a hybrid immersive human–computer interface for data analysis tasks. Using IVR maximizes human senses to capitalize on our instinctual perception of the physical environment, thereby leveraging our innate ability to recognize patterns and visualize thresholds crucial to reducing erroneous outcomes. In this first use of IVR as an immersive analytic tool for spectral data, we examine an integrated IVR real-time model selection algorithm for a recent model updating method that adapts a model from the original calibration domain to predict samples from shifted target domains. Using near-infrared data, analyte prediction errors from IVR-selected models are reduced compared to errors using an established autonomous model selection approach. Results demonstrate the viability of IVR as a human data analysis interface for spectral data analysis including classification problems.