Multiclass threshold-based classification and model evaluation

• URL: http://arxiv.org/abs/2511.21794v1
• Date: Wed, 26 Nov 2025 17:00:00 GMT
• Title: Multiclass threshold-based classification and model evaluation
• Authors: Edoardo Legnaro, Sabrina Guastavino, Francesco Marchetti,
• Abstract summary: We introduce a threshold-based framework for multiclass classification that generalizes the standard argmax rule.<n>Experiments show that multidimensional threshold tuning yields performance improvements across various networks and datasets.
• Score: 4.014524824655106
• License: http://creativecommons.org/licenses/by-nc-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 \textit{a posteriori} optimization of the classification score by means of threshold tuning, as usually carried out in the binary setting, thus allowing for a further refinement of the prediction capability of any network. Our experiments show indeed that multidimensional threshold tuning yields performance improvements across various networks and datasets. Moreover, we derive a multiclass ROC analysis based on \emph{ROC clouds} -- the attainable (FPR,TPR) operating points induced by a single multiclass threshold -- and summarize them via a \emph{Distance From Point} (DFP) score to $(0,1)$. This yields a coherent alternative to standard One-vs-Rest (OvR) curves and aligns with the observed tuning gains.

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