Multiclass threshold-based classification

• URL: http://arxiv.org/abs/2505.11276v1
• Date: Fri, 16 May 2025 14:11:26 GMT
• Title: Multiclass threshold-based classification
• Authors: Francesco Marchetti, Edoardo Legnaro, Sabrina Guastavino,
• Abstract summary: We introduce a threshold-based framework for multiclass classification that generalizes the standard argmax rule.<n>We show that the multidimensional threshold-based setting yields consistent performance improvements across various networks and datasets.
• Score: 2.66269503676104
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In this paper, we introduce a threshold-based framework for multiclass classification that generalizes the standard argmax rule. This is done by replacing the probabilistic interpretation of softmax outputs with a geometric one on the multidimensional simplex, where the classification depends on a multidimensional threshold. This change of perspective enables for any trained classification network an a posteriori optimization of the classification score by means of threshold tuning, as usually carried out in the binary setting. This allows a further refinement of the prediction capability of any network. Moreover, this multidimensional threshold-based setting makes it possible to define score-oriented losses, which are based on the interpretation of the threshold as a random variable. Our experiments show that the multidimensional threshold tuning yields consistent performance improvements across various networks and datasets, and that the proposed multiclass score-oriented losses are competitive with standard loss functions, resembling the advantages observed in the binary case.

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