Abstract
A reject rule devised for a neural classifier based on the Learning Vector Quantization (LVQ) paradigm is presented. The reject option is carried out adaptively to the specific application domain. It is assumed that a performance function P is defined which, taking into account the requirements of a given application expressed in terms of classification, misclassification and reject costs, evaluates the quality of the classification. Under this assumption the optimal reject threshold value, determining the best trade-off between reject rate and misclassification rate, is the one for which the function P reaches its absolute maximum. Implementation and performance of the rule are illustrated.
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© 1995 Springer-Verlag Berlin Heidelberg
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Cordella, L.P., De Stefano, C., Sansone, C., Vento, M. (1995). An adaptive reject option for LVQ classifiers. In: Braccini, C., DeFloriani, L., Vernazza, G. (eds) Image Analysis and Processing. ICIAP 1995. Lecture Notes in Computer Science, vol 974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60298-4_238
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DOI: https://doi.org/10.1007/3-540-60298-4_238
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