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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Local Rejection Strategies for Learning Vector Quantization
verfasst von : Lydia Fischer, Barbara Hammer, Heiko Wersing
Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2014
Verlag: Springer International Publishing
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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.