A Perspective on Using Immersive Analytics With Virtual Reality for One‐Class Classification Decisions

ABSTRACT Multisensory tools are beginning to reformat research and education in chemistry and many other fields. For example, translating infrared spectra into sound (sonification) can unveil molecular facts that the eye might miss. Tactile approaches are used with 3‐D printed scientific data such as electrophoresis gels. These scientific advances are expanding the way we study data and are part of a broader area known as immersive analytics. While immersive analytics cover all the human senses for data immersion, this perspective focuses on data visualization in virtual reality (VR). By visualizing chemometric data information in VR, a human can use their extensively trained pattern recognition and problem‐solving skills to make final analysis decisions. As an example, presented is a feasibility study for one‐class classification with a focus on correcting samples machine learning (ML) identified as false positives (FPs) and false negatives (FNs) that typically reside in the gray zone (class fringe samples) to respective true negatives (TNs) and positives (TPs). Conversely, while not expected in a well‐designed VR universe, it is possible that TP and TN prediction samples in the gray zone could be classified as respective FNs and FPs by decisions in VR. Results are presented for three datasets showing the feasibility of using VR for classification decisions. These datasets are clam contamination and two cancer detection situations. Some brief comments on the potential of using VR to identify local structure within a class are also provided using a quantitative structure–activity relationship (QSAR) dataset.