Optimum Reject Options for Prototype-based Classification
This work addresses the challenge of enhancing classification reliability for prototype-based methods, though it is incremental as it builds on existing reject option frameworks.
The paper tackles the problem of improving prototype-based classifiers by analyzing optimal reject strategies using distance or probabilistic measures, and finds that local reject options significantly benefit simple models, achieving accuracy-reject curves comparable to state-of-the-art SVM classifiers.
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.