title: Optimal threshold estimation for binary classifiers using game theory.
abstract: Many bioinformatics algorithms can be understood as binary classifiers. They are usually compared using the area under the receiver operating characteristic (ROC) curve. On the other hand, choosing the best threshold for practical use is a complex task, due to uncertain and context-dependent skews in the abundance of positives in nature and in the yields/costs for correct/incorrect classification. We argue that considering a classifier as a player in a zero-sum game allows us to use the minimax principle from game theory to determine the optimal operating point. The proposed classifier threshold corresponds to the intersection between theROCcurve and the descending diagonal inROCspace and yields a minimax accuracy of 1-FPR. Our proposal can be readily implemented in practice, and reveals that the empirical condition for threshold estimation of "specificity equals sensitivity" maximizes robustness against uncertainties in the abundance of positives in nature and classification costs.
authors.affiliation: Protein Physiology Laboratory, University of Buenos Aires, Buenos Aires, Argentina.
authors.forename: Ignacio Enrique
authors.initials: IE
authors.lastname: Sanchez
keywords: Binary classifier; ROC curve; accuracy; class imbalance; game theory; minimax principle.; optimal cutoff; optimal threshold