Confusion Matrices: A Unified Theory
The confusion matrix is a key tool for understanding and evaluating models in supervised classification problems. Various matrices are proposed depending on the problem framework: single-label, multi-label, or even soft-label restricted to probability distributions. However, most of these approaches...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10769075/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850259323537588224 |
|---|---|
| author | Johan Erbani Pierre-Edouard Portier Elod Egyed-Zsigmond Diana Nurbakova |
| author_facet | Johan Erbani Pierre-Edouard Portier Elod Egyed-Zsigmond Diana Nurbakova |
| author_sort | Johan Erbani |
| collection | DOAJ |
| description | The confusion matrix is a key tool for understanding and evaluating models in supervised classification problems. Various matrices are proposed depending on the problem framework: single-label, multi-label, or even soft-label restricted to probability distributions. However, most of these approaches are not compatible with each other and lack theoretical justification. Leveraging optimal transport theory and the principle of maximum entropy, we propose a unique confusion matrix applicable across single, multi, and soft-label contexts. The Transport-based Confusion Matrix (TCM) extends the classic Confusion Matrix (CM), being identical in the single-label context. TCM introduces a comprehensive, theory-supported description of previously inaccessible errors, thereby enhancing the consistency and scope of machine learning evaluation. |
| format | Article |
| id | doaj-art-6f1e904c9cc6491ca31a2ddd56cc6ca6 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6f1e904c9cc6491ca31a2ddd56cc6ca62025-08-20T01:55:52ZengIEEEIEEE Access2169-35362024-01-011218137218141910.1109/ACCESS.2024.350719910769075Confusion Matrices: A Unified TheoryJohan Erbani0https://orcid.org/0009-0000-2717-608XPierre-Edouard Portier1https://orcid.org/0000-0002-6439-9466Elod Egyed-Zsigmond2https://orcid.org/0000-0002-1218-8026Diana Nurbakova3https://orcid.org/0000-0002-6620-7771INSA Lyon, CNRS, UCBL, LIRIS, UMR 5205, Université de Lyon, Villeurbanne, FranceCaisse d’Epargne Rhône-Alpes, Paris, FranceINSA Lyon, CNRS, UCBL, LIRIS, UMR 5205, Université de Lyon, Villeurbanne, FranceINSA Lyon, CNRS, UCBL, LIRIS, UMR 5205, Université de Lyon, Villeurbanne, FranceThe confusion matrix is a key tool for understanding and evaluating models in supervised classification problems. Various matrices are proposed depending on the problem framework: single-label, multi-label, or even soft-label restricted to probability distributions. However, most of these approaches are not compatible with each other and lack theoretical justification. Leveraging optimal transport theory and the principle of maximum entropy, we propose a unique confusion matrix applicable across single, multi, and soft-label contexts. The Transport-based Confusion Matrix (TCM) extends the classic Confusion Matrix (CM), being identical in the single-label context. TCM introduces a comprehensive, theory-supported description of previously inaccessible errors, thereby enhancing the consistency and scope of machine learning evaluation.https://ieeexplore.ieee.org/document/10769075/Classificationevaluationmachine learningmulti-label confusion matrixoptimal transportsingle-label confusion matrix |
| spellingShingle | Johan Erbani Pierre-Edouard Portier Elod Egyed-Zsigmond Diana Nurbakova Confusion Matrices: A Unified Theory IEEE Access Classification evaluation machine learning multi-label confusion matrix optimal transport single-label confusion matrix |
| title | Confusion Matrices: A Unified Theory |
| title_full | Confusion Matrices: A Unified Theory |
| title_fullStr | Confusion Matrices: A Unified Theory |
| title_full_unstemmed | Confusion Matrices: A Unified Theory |
| title_short | Confusion Matrices: A Unified Theory |
| title_sort | confusion matrices a unified theory |
| topic | Classification evaluation machine learning multi-label confusion matrix optimal transport single-label confusion matrix |
| url | https://ieeexplore.ieee.org/document/10769075/ |
| work_keys_str_mv | AT johanerbani confusionmatricesaunifiedtheory AT pierreedouardportier confusionmatricesaunifiedtheory AT elodegyedzsigmond confusionmatricesaunifiedtheory AT diananurbakova confusionmatricesaunifiedtheory |