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| Main Author: | |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2101.00583 |
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| _version_ | 1866912044013322240 |
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| author | Dery, Lihi |
| author_facet | Dery, Lihi |
| contents | We survey multi-label ranking tasks, specifically multi-label classification and label ranking classification. We highlight the unique challenges, and re-categorize the methods, as they no longer fit into the traditional categories of transformation and adaptation. We survey developments in the last demi-decade, with a special focus on state-of-the-art methods in deep learning multi-label mining, extreme multi-label classification and label ranking. We conclude by offering a few future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2101_00583 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Multi-label Ranking: Mining Multi-label and Label Ranking Data Dery, Lihi Machine Learning We survey multi-label ranking tasks, specifically multi-label classification and label ranking classification. We highlight the unique challenges, and re-categorize the methods, as they no longer fit into the traditional categories of transformation and adaptation. We survey developments in the last demi-decade, with a special focus on state-of-the-art methods in deep learning multi-label mining, extreme multi-label classification and label ranking. We conclude by offering a few future research directions. |
| title | Multi-label Ranking: Mining Multi-label and Label Ranking Data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2101.00583 |