Specified Certainty Classification, with Application to Read Classification for Reference-Guided Metagenomic Assembly

TL;DR

This paper introduces Specified Certainty Classification (SCC), a new framework for classifiers that incorporate uncertainty, enabling decisions to meet a specified confidence level and providing deeper insights into classifier behavior, demonstrated through genomic and vaccination data.

Contribution

The paper proposes SCC as a novel classification paradigm that explicitly manages uncertainty and guarantees decision certainty levels, applicable across diverse domains.

Findings

SCC allows decisions to meet predefined certainty thresholds.

SCC provides insights into classifier behavior through uncertainty analysis.

Demonstrated effectiveness on genomic read classification and COVID-19 vaccination data.

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.