A Unified Algebraic Framework for Classification Performance Evaluation

arXiv:2607.04028v1 Announce Type: cross Abstract: We propose a unified algebraic framework for classification performance evaluation that encompasses binary, multiclass, multilabel, ordinal, hierarchical, cost-sensitive, and soft-label settings within a single formalism. The foundation is a representation of actual and predicted labels as binary indicator matrices, combined with three aggregation operators -- global, column-wise, and row-wise -- that correspond exactly to micro, macro/weighted, and exemplar averaging. Any binary performance measure expressed in terms of true/positive/negative counts extends automatically to all settings by substituting these operators, generating multiclass and multilabel versions without measure-specific derivations. The framework further accommodates soft classifier outputs via argmax or thresholding, soft ground truth via triangular norms, ordinal classification via membership functions or cumulative encodings, and cost-sensitive evaluation via a cost matrix that subsumes MAE and MSE as special cases. We establish several theoretical results: micro-averaging equals denominator-weighted macro-averaging; the product t-norm is the unique one preserving the confusion-matrix partition; skew-invariant measures are characterised as functions of recall and specificity; and micro-precision, micro-recall, and micro-F_1 are all equal to accuracy in multiclass settings. Empirical illustrations on synthetic and real data confirm the theoretical findings.

Source: arXiv cs.AI | 2026-07-07