Estimating Classification Confidence Using Kernel Densities

Peter Salamon, David Salamon, V. Adrian Cantu, Michelle An, Tyler Perry, Robert A. Edwards, Anca M. Segall

Research output: Working paper/PreprintPreprint

Abstract


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.
Original languageEnglish
PublisherArxiv
Number of pages14
DOIs
Publication statusSubmitted - 15 Sep 2022

Keywords

  • confidence calibration
  • top-label confidence calibration
  • bioinformatics
  • machine learning
  • exploratory machine learning

Fingerprint

Dive into the research topics of 'Estimating Classification Confidence Using Kernel Densities'. Together they form a unique fingerprint.

Cite this