Specified Certainty Classification, with Application to Read Classification for Reference-Guided Metagenomic Assembly
Quantitative Methods
2021-09-29 v2 Machine Learning
Abstract
Specified Certainty Classification (SCC) is a new paradigm for employing classifiers whose outputs carry uncertainties, typically in the form of Bayesian posterior probabilities. By allowing the classifier output to be less precise than one of a set of atomic decisions, SCC allows all decisions to achieve a specified level of certainty, as well as provides insights into classifier behavior by examining all decisions that are possible. Our primary illustration is read classification for reference-guided genome assembly, but we demonstrate the breadth of SCC by also analyzing COVID-19 vaccination data.
Cite
@article{arxiv.2109.06677,
title = {Specified Certainty Classification, with Application to Read Classification for Reference-Guided Metagenomic Assembly},
author = {Alan F. Karr and Jason Hauzel and Prahlad Menon and Adam A. Porter and Marcel Schaefer},
journal= {arXiv preprint arXiv:2109.06677},
year = {2021}
}