Minimal Learning Machine for Multi-Label Learning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866916506393116672 |
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| author | Hämäläinen, Joonas Hubermont, Antoine Souza, Amauri Mattos, César L. C. Gomes, João P. P. Kärkkäinen, Tommi |
| author_facet | Hämäläinen, Joonas Hubermont, Antoine Souza, Amauri Mattos, César L. C. Gomes, João P. P. Kärkkäinen, Tommi |
| contents | Distance-based supervised method, the minimal learning machine, constructs a predictive model from data by learning a mapping between input and output distance matrices. In this paper, we propose new methods and evaluate how their core component, the distance mapping, can be adapted to multi-label learning. The proposed approach is based on combining the distance mapping with an inverse distance weighting. Although the proposal is one of the simplest methods in the multi-label learning literature, it achieves state-of-the-art performance for small to moderate-sized multi-label learning problems. In addition to its simplicity, the proposed method is fully deterministic: Its hyper-parameter can be selected via ranking loss-based statistic which has a closed form, thus avoiding conventional cross-validation-based hyper-parameter tuning. In addition, due to its simple linear distance mapping-based construction, we demonstrate that the proposed method can assess the uncertainty of the predictions for multi-label classification, which is a valuable capability for data-centric machine learning pipelines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_05518 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Minimal Learning Machine for Multi-Label Learning Hämäläinen, Joonas Hubermont, Antoine Souza, Amauri Mattos, César L. C. Gomes, João P. P. Kärkkäinen, Tommi Machine Learning Distance-based supervised method, the minimal learning machine, constructs a predictive model from data by learning a mapping between input and output distance matrices. In this paper, we propose new methods and evaluate how their core component, the distance mapping, can be adapted to multi-label learning. The proposed approach is based on combining the distance mapping with an inverse distance weighting. Although the proposal is one of the simplest methods in the multi-label learning literature, it achieves state-of-the-art performance for small to moderate-sized multi-label learning problems. In addition to its simplicity, the proposed method is fully deterministic: Its hyper-parameter can be selected via ranking loss-based statistic which has a closed form, thus avoiding conventional cross-validation-based hyper-parameter tuning. In addition, due to its simple linear distance mapping-based construction, we demonstrate that the proposed method can assess the uncertainty of the predictions for multi-label classification, which is a valuable capability for data-centric machine learning pipelines. |
| title | Minimal Learning Machine for Multi-Label Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2305.05518 |