Specified Certainty Classification, with Application to Read Classification for Reference-Guided Metagenomic Assembly
This addresses the need for reliable decision-making in domains like genomics and public health by providing a method to handle classifier uncertainties, though it appears incremental as it builds on existing Bayesian probability frameworks.
The paper tackles the problem of classifiers with uncertain outputs by introducing Specified Certainty Classification (SCC), a new paradigm that allows decisions to achieve a specified certainty level, and demonstrates its application in read classification for genome assembly and COVID-19 vaccination data analysis.
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.