ClassHeatVis: Interpretable Probabilistic Classification via Heatmap Visualization for AIS Radio Frequency Fingerprinting

In a complex context with many classes and instances, synthetic classification evaluation techniques (e.g. classical measures made from confusion matrix) often obscure important information. Such information usually enables experts to achieve well-informed decisions, particularly under strong temporal constraints. Visual analysis thus appears to be a promising additional support. We thus propose an approach to visualize the results of a multi-class classifier at instance level in the form of a bar chart and a heatmap by class. The novelty of this approach lies in presenting visually to experts the entirety of the classifier's predictions, allowing them to synthesize these outcomes by themselves. By doing so, they can dynamically adjust their confidence levels and make informed decisions. We identify several user objectives and illustrate the interest and validity of our approach on AIS (Automatic Identification System) radio frequency fingerprinting.