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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2306.09461 |
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| _version_ | 1866914676767457280 |
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| author | Riehl, Kevin Neunteufel, Michael Hemberg, Martin |
| author_facet | Riehl, Kevin Neunteufel, Michael Hemberg, Martin |
| contents | In this work we propose a novel concept of a hierarchical confusion matrix, opening the door for popular confusion matrix based (flat) evaluation measures from binary classification problems, while considering the peculiarities of hierarchical classification problems. We develop the concept to a generalized form and prove its applicability to all types of hierarchical classification problems including directed acyclic graphs, multi path labelling, and non mandatory leaf node prediction. Finally, we use measures based on the novel confusion matrix to evaluate models within a benchmark for three real world hierarchical classification applications and compare the results to established evaluation measures. The results outline the reasonability of this approach and its usefulness to evaluate hierarchical classification problems. The implementation of hierarchical confusion matrix is available on GitHub. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_09461 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Hierarchical confusion matrix for classification performance evaluation Riehl, Kevin Neunteufel, Michael Hemberg, Martin Machine Learning In this work we propose a novel concept of a hierarchical confusion matrix, opening the door for popular confusion matrix based (flat) evaluation measures from binary classification problems, while considering the peculiarities of hierarchical classification problems. We develop the concept to a generalized form and prove its applicability to all types of hierarchical classification problems including directed acyclic graphs, multi path labelling, and non mandatory leaf node prediction. Finally, we use measures based on the novel confusion matrix to evaluate models within a benchmark for three real world hierarchical classification applications and compare the results to established evaluation measures. The results outline the reasonability of this approach and its usefulness to evaluate hierarchical classification problems. The implementation of hierarchical confusion matrix is available on GitHub. |
| title | Hierarchical confusion matrix for classification performance evaluation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2306.09461 |