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Learning vector quantization classifiers for ROC-optimization

Author

Listed:
  • T. Villmann

    (University of Applied Sciences Mittweida)

  • M. Kaden

    (University of Applied Sciences Mittweida)

  • W. Hermann

    (Paracelsus-Klinikum Zwickau)

  • M. Biehl

    (University Groningen)

Abstract

This paper proposes a variant of the generalized learning vector quantizer (GLVQ) optimizing explicitly the area under the receiver operating characteristics (ROC) curve for binary classification problems instead of the classification accuracy, which is frequently not appropriate for classifier evaluation. This is particularly important in case of overlapping class distributions, when the user has to decide about the trade-off between high true-positive and good false-positive performance. The model keeps the idea of learning vector quantization based on prototypes by stochastic gradient descent learning. For this purpose, a GLVQ-based cost function is presented, which describes the area under the ROC-curve in terms of the sum of local discriminant functions. This cost function reflects the underlying rank statistics in ROC analysis being involved into the design of the prototype based discriminant function. The resulting learning scheme for the prototype vectors uses structured inputs, i.e. ordered pairs of data vectors of both classes.

Suggested Citation

  • T. Villmann & M. Kaden & W. Hermann & M. Biehl, 2018. "Learning vector quantization classifiers for ROC-optimization," Computational Statistics, Springer, vol. 33(3), pages 1173-1194, September.
    • Handle: RePEc:spr:compst:v:33:y:2018:i:3:d:10.1007_s00180-016-0678-y
    DOI: 10.1007/s00180-016-0678-y
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    References listed on IDEAS

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    1. Jens Keilwagen & Ivo Grosse & Jan Grau, 2014. "Area under Precision-Recall Curves for Weighted and Unweighted Data," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-13, March.
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    Cited by:

    1. Hans A. Kestler & Bernd Bischl & Matthias Schmid, 2018. "Proceedings of Reisensburg 2014–2015," Computational Statistics, Springer, vol. 33(3), pages 1125-1126, September.

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