Optimum Reject Options for Prototype-based Classification

TL;DR

This paper investigates optimal reject strategies for prototype-based classifiers, comparing global and local thresholds, and introduces efficient algorithms to enhance classification performance with reject options.

Contribution

It develops a polynomial-time dynamic programming algorithm and a linear approximation for optimal reject thresholds in prototype classifiers.

Findings

Local reject options improve simple classifiers significantly.

Advanced classifiers show less benefit from local reject options.

Achieves accuracy-reject performance comparable to SVMs with state-of-the-art reject strategies.

Abstract

We analyse optimum reject strategies for prototype-based classifiers and real-valued rejection measures, using the distance of a data point to the closest prototype or probabilistic counterparts. We compare reject schemes with global thresholds, and local thresholds for the Voronoi cells of the classifier. For the latter, we develop a polynomial-time algorithm to compute optimum thresholds based on a dynamic programming scheme, and we propose an intuitive linear time, memory efficient approximation thereof with competitive accuracy. Evaluating the performance in various benchmarks, we conclude that local reject options are beneficial in particular for simple prototype-based classifiers, while the improvement is less pronounced for advanced models. For the latter, an accuracy-reject curve which is comparable to support vector machine classifiers with state of the art reject options can be reached.