2015/03/23 by Lydia Fischer, Barbara Hammer, Fischer, Lydia +3
Computer Science · #Data Stream Mining Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Machine Learning and Data Classification #cs.LG
paper · pdf · doi:10.48550/arxiv.1503.06549
19 pages
arxiv created 2015/03/23 · openalex publication_date 2015/03/23 · arxiv updated 2015/03/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.