Estimating Classification Confidence Using Kernel Densities
This work addresses the challenge of reliable confidence estimation in machine learning for domains like bioinformatics, where dataset curation is uncertain, but it is incremental as it builds on existing calibration methods.
The paper tackles the problem of post-hoc confidence calibration for exploratory classification tasks, where categories may have limited examples or unclear validity, by advocating for a top-label approach and introducing four new algorithms, including one using kernel density ratios with a robust bandwidth selection method, achieving improved calibration on both bioinformatics and MNIST benchmarks.
This paper investigates the post-hoc calibration of confidence for "exploratory" machine learning classification problems. The difficulty in these problems stems from the continuing desire to push the boundaries of which categories have enough examples to generalize from when curating datasets, and confusion regarding the validity of those categories. We argue that for such problems the "one-versus-all" approach (top-label calibration) must be used rather than the "calibrate-the-full-response-matrix" approach advocated elsewhere in the literature. We introduce and test four new algorithms designed to handle the idiosyncrasies of category-specific confidence estimation. Chief among these methods is the use of kernel density ratios for confidence calibration including a novel, bulletproof algorithm for choosing the bandwidth. We test our claims and explore the limits of calibration on a bioinformatics application (PhANNs) as well as the classic MNIST benchmark. Finally, our analysis argues that post-hoc calibration should always be performed, should be based only on the test dataset, and should be sanity-checked visually.