Abstract
Prototype-based classification is mainly influenced by the family of learning vector quantizers (LVQ) as introduced by Kohonen. The main goal is to optimize the classification accuracy while the prototypes explore the class distribution in the data space. Recent variants can deal also with dissimilarity data, i.e. only the dissimilarities between the data objects are given. Otherwise, classification accuracy may be not appropriate to judge the classification performance, for example for imbalanced data or in medical applications, where frequently sensitivity and specificity are favored. In this paper we develop a median LVQ-variant optimizing those statistical classification evaluation measures, if only dissimilarity data are available. Thus, the presented approach is the discrete counterpart of a recently proposed LVQ-approach for optimization of statistical measures in case of vectorial data. For this purpose, we make use of a probabilistic description of the classification decision proposed in Robust Soft LVQ.
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Notes
- 1.
For a mathematical definition of a dissimilarity measure we refer to [20].
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Nebel, D., Villmann, T. (2016). Optimization of Statistical Evaluation Measures for Classification by Median Learning Vector Quantization. In: Merényi, E., Mendenhall, M., O'Driscoll, P. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 428. Springer, Cham. https://doi.org/10.1007/978-3-319-28518-4_25
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