A Unified Algebraic Framework for Classification Performance Evaluation
We propose a unified algebraic framework for classification performance evaluation covering binary, multiclass, multilabel, ordinal, hierarchical, cost-sensitive, and soft-label settings. Actual and predicted labels are represented as binary indicator matrices, where three aggregation operators (global, column-wise, row-wise) correspond directly to micro, macro/weighted, and exemplar averaging. Any binary measure expressed in terms of the four confusion-matrix counts extends to all these settings by substituting an operator, with no measure-specific derivation. We show that structural properties governing an extension are derivable from the binary formula. Micro-averaging equals denominat...
| Authors | Ronaldo C. Prati |
|---|---|
| Primary category | cs.LG |
| Published | 4 July 2026 |
| Last updated at source | 25 August 2026 |
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