Zero-failure testing of binary classifiers

TL;DR

This paper introduces a zero-failure testing method for binary classifiers that emphasizes asymmetric error treatment, enabling the creation of increasingly difficult test sets, demonstrated through an age estimation application.

Contribution

The paper presents a novel zero-failure testing approach that constructs nested test sets with asymmetric error considerations, improving classifier evaluation in critical applications.

Findings

Effective in age estimation for legal compliance

Allows for nested, progressively challenging test sets

Highlights importance of asymmetric error handling

Abstract

We propose using performance metrics derived from zero-failure testing to assess binary classifiers. The principal characteristic of the proposed approach is the asymmetric treatment of the two types of error. In particular, we construct a test set consisting of positive and negative samples, set the operating point of the binary classifier at the lowest value that will result to correct classifications of all positive samples, and use the algorithm's success rate on the negative samples as a performance measure. A property of the proposed approach, setting it apart from other commonly used testing methods, is that it allows the construction of a series of tests of increasing difficulty, corresponding to a nested sequence of positive sample test sets. We illustrate the proposed method on the problem of age estimation for determining whether a subject is above a legal age threshold, a problem that exemplifies the asymmetry of the two types of error. Indeed, misclassifying an under-aged subject is a legal and regulatory issue, while misclassifications of people above the legal age is an efficiency issue primarily concerning the commercial user of the age estimation system.