Multi-label Classification under Uncertainty: A Tree-based Conformal Prediction Approach
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910428693528576 |
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| author | Tyagi, Chhavi Guo, Wenge |
| author_facet | Tyagi, Chhavi Guo, Wenge |
| contents | Multi-label classification is a common challenge in various machine learning applications, where a single data instance can be associated with multiple classes simultaneously. The current paper proposes a novel tree-based method for multi-label classification using conformal prediction and multiple hypothesis testing. The proposed method employs hierarchical clustering with labelsets to develop a hierarchical tree, which is then formulated as a multiple-testing problem with a hierarchical structure. The split-conformal prediction method is used to obtain marginal conformal $p$-values for each tested hypothesis, and two \textit{hierarchical testing procedures} are developed based on marginal conformal $p$-values, including a hierarchical Bonferroni procedure and its modification for controlling the family-wise error rate. The prediction sets are thus formed based on the testing outcomes of these two procedures. We establish a theoretical guarantee of valid coverage for the prediction sets through proven family-wise error rate control of those two procedures. We demonstrate the effectiveness of our method in a simulation study and two real data analysis compared to other conformal methods for multi-label classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_19472 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-label Classification under Uncertainty: A Tree-based Conformal Prediction Approach Tyagi, Chhavi Guo, Wenge Methodology Multi-label classification is a common challenge in various machine learning applications, where a single data instance can be associated with multiple classes simultaneously. The current paper proposes a novel tree-based method for multi-label classification using conformal prediction and multiple hypothesis testing. The proposed method employs hierarchical clustering with labelsets to develop a hierarchical tree, which is then formulated as a multiple-testing problem with a hierarchical structure. The split-conformal prediction method is used to obtain marginal conformal $p$-values for each tested hypothesis, and two \textit{hierarchical testing procedures} are developed based on marginal conformal $p$-values, including a hierarchical Bonferroni procedure and its modification for controlling the family-wise error rate. The prediction sets are thus formed based on the testing outcomes of these two procedures. We establish a theoretical guarantee of valid coverage for the prediction sets through proven family-wise error rate control of those two procedures. We demonstrate the effectiveness of our method in a simulation study and two real data analysis compared to other conformal methods for multi-label classification. |
| title | Multi-label Classification under Uncertainty: A Tree-based Conformal Prediction Approach |
| topic | Methodology |
| url | https://arxiv.org/abs/2404.19472 |