Abstract
Classification with rejection is well understood for classifiers which provide explicit class probabilities. The situation is more complicated for popular deterministic classifiers such as learning vector quantisation schemes: albeit reject options using simple distance-based geometric measures were proposed [4], their local scaling behaviour is unclear for complex problems. Here, we propose a local threshold selection strategy which automatically adjusts suitable threshold values for reject options in prototype-based classifiers from given data. We compare this local threshold strategy to a global choice on artificial and benchmark data sets; we show that local thresholds enhance the classification results in comparison to global ones, and they better approximate optimal Bayesian rejection in cases where the latter is available.
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Fischer, L., Hammer, B., Wersing, H. (2014). Local Rejection Strategies for Learning Vector Quantization. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_71
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DOI: https://doi.org/10.1007/978-3-319-11179-7_71
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