Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection

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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