Learning vector quantization classifiers for ROC-optimization

T. Villmann (), M. Kaden, W. Hermann and M. Biehl
Additional contact information
T. Villmann: University of Applied Sciences Mittweida
M. Kaden: University of Applied Sciences Mittweida
W. Hermann: Paracelsus-Klinikum Zwickau
M. Biehl: University Groningen

Computational Statistics, 2018, vol. 33, issue 3, No 5, 1173-1194

Abstract: 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.

Keywords: Learning vector quantization; ROC analysis; AUC optimization (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00180-016-0678-y

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