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Erschienen in:
Human Brainnetome Atlas and Its Potential Applications in Brain-Inspired Computing Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Sophisticated LVQ Classification Models - Beyond Accuracy Optimization

verfasst von : Thomas Villmann

Erschienen in: Brain-Inspired Computing

Verlag: Springer International Publishing

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Abstract

Learning vector quantization models (LVQ) belong to the most successful machine learning classifiers. LVQs are intuitively designed and generally allow an easy interpretation according to the class dependent prototype principle. Originally, LVQs try to optimize the classification accuracy during adaptation, which can be misleading in case of imbalanced data. Further, it might be required by the application that other statistical classification evaluation measures should be considered, e.g. sensitivity and specificity like frequently demanded in bio-medical applications. In this article we present recent approaches, how to modify LVQ to integrate those sophisticated evaluation measures as objectives to be optimized. Particularly, we show that all differentiable functions built fro contingency tables can be incorporated into a LVQ-scheme as well as receiver operating characteristic curve optimization.

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Vorheriges Kapitel Bio-Inspired Filters for Audio Analysis
Nächstes Kapitel Classification of FDG-PET Brain Data by Generalized Matrix Relevance LVQ
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Metadaten
Titel
Sophisticated LVQ Classification Models - Beyond Accuracy Optimization
verfasst von
Thomas Villmann
Copyright-Jahr
2016
Buch
Brain-Inspired Computing

Print ISBN: 978-3-319-50861-0

Electronic ISBN: 978-3-319-50862-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-50862-7_9

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