Receiver operating characteristic curves are a mainstay in binary classification and have seen widespread use from their inception characterizing radar receivers in 1941. Widely used and accepted, the ROC curve is the default option for many application spaces. Building on prior work the Prognostics and Health Management community naturally adopted ROC curves to visualize classifier performance. While the ROC curve is perhaps the best known visualization of binary classifier performance it is not the only game in town. Authors from across various STEM fields have published works extolling various other metrics and visualizations in binary classifier performance evaluation. These include, but are not limited
to, the precision recall characteristic curve, area under the curve metrics, bookmaker informedness and markedness. This paper will review these visualizations and metrics, provide references for more exhaustive treatments on them, and provide a case study of their use on an imbalanced prognostic health management data-set. Prognostic health management binary classification problems are often highly imbalanced with a low prevalence of positive (faulty) cases compared to negative (nominal/healthy) cases. In the presented data-set, time domain accelerometer data for a series of run-to-failure ball-on-disk scuffing tests provide a case where the vast majority of data, > 94%, is from nominally healthy data instances. A condition indicator algorithm targeting the hypothesized physical system response is validated compared to less informed classifiers. Several characteristic curves are then used to showcase the performance improvement of the physics informed condition indicator.
How to Cite
ROC, PRC, Scuffing, Wear, Fault, Envelope, Optimization, Decision logic, Amplitude demodulation
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