Abstract
We consider a reject option for prototype-based Learning Vector Quantization (LVQ), which facilitates the detection of outliers in the data during the classification process. The rejection mechanism is based on a distance-based criterion and the corresponding threshold is automatically adjusted in the training phase according to pre-defined rejection costs. The adaptation of LVQ prototypes is simultaneously guided by the complementary aims of low classification error, faithful representation of the observed data, and low total rejection costs.
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Villmann, T., Kaden, M., Nebel, D., Biehl, M. (2015). Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9257. Springer, Cham. https://doi.org/10.1007/978-3-319-23117-4_66
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DOI: https://doi.org/10.1007/978-3-319-23117-4_66
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