A weighted difference loss approach for enhancing multi-label classification
Conventional multi-label classification methods often fail to capture the dynamic relationships and relative intensity shifts between labels, treating them as independent entities. This limitation is particularly detrimental in tasks like sentiment analysis where emotions co-occur in nuanced proport...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Research
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/120145/ http://psasir.upm.edu.my/id/eprint/120145/1/120145.pdf |
| _version_ | 1848868122592804864 |
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| author | Qiong, Hu Azmi Murad, Masrah Azrifah Azman, Azreen Nasharuddin, Nurul Amelina |
| author_facet | Qiong, Hu Azmi Murad, Masrah Azrifah Azman, Azreen Nasharuddin, Nurul Amelina |
| author_sort | Qiong, Hu |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Conventional multi-label classification methods often fail to capture the dynamic relationships and relative intensity shifts between labels, treating them as independent entities. This limitation is particularly detrimental in tasks like sentiment analysis where emotions co-occur in nuanced proportions. To address this, we introduce a novel Weighted Difference Loss (WDL) framework. WDL operates on three core principles: (1) transforming labels into a normalized distribution to model their relative proportions; (2) computing learnable, weighted differences across this distribution to explicitly capture inter-label dynamics and trends; and (3) employing a label-shuffling augmentation to ensure the model learns intrinsic, order-invariant relationships. Our framework not only achieves state-of-the-art performance on four public benchmarks, but more importantly, it substantially improves the recognition of minority classes. This demonstrates the framework’s ability to learn from sparse data by effectively leveraging the underlying label structure, offering a robust, loss-driven alternative to complex architectural modifications. |
| first_indexed | 2025-11-15T14:47:22Z |
| format | Article |
| id | upm-120145 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:47:22Z |
| publishDate | 2025 |
| publisher | Nature Research |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1201452025-09-24T01:55:26Z http://psasir.upm.edu.my/id/eprint/120145/ A weighted difference loss approach for enhancing multi-label classification Qiong, Hu Azmi Murad, Masrah Azrifah Azman, Azreen Nasharuddin, Nurul Amelina Conventional multi-label classification methods often fail to capture the dynamic relationships and relative intensity shifts between labels, treating them as independent entities. This limitation is particularly detrimental in tasks like sentiment analysis where emotions co-occur in nuanced proportions. To address this, we introduce a novel Weighted Difference Loss (WDL) framework. WDL operates on three core principles: (1) transforming labels into a normalized distribution to model their relative proportions; (2) computing learnable, weighted differences across this distribution to explicitly capture inter-label dynamics and trends; and (3) employing a label-shuffling augmentation to ensure the model learns intrinsic, order-invariant relationships. Our framework not only achieves state-of-the-art performance on four public benchmarks, but more importantly, it substantially improves the recognition of minority classes. This demonstrates the framework’s ability to learn from sparse data by effectively leveraging the underlying label structure, offering a robust, loss-driven alternative to complex architectural modifications. Nature Research 2025 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/120145/1/120145.pdf Qiong, Hu and Azmi Murad, Masrah Azrifah and Azman, Azreen and Nasharuddin, Nurul Amelina (2025) A weighted difference loss approach for enhancing multi-label classification. Scientific Reports, 15 (1). art. no. 25052. pp. 1-13. ISSN 2045-2322 https://www.nature.com/articles/s41598-025-09883-2?error=cookies_not_supported&code=b1a5fc06-d472-4597-8bfb-f0f263aebf2d 10.1038/s41598-025-09883-2 |
| spellingShingle | Qiong, Hu Azmi Murad, Masrah Azrifah Azman, Azreen Nasharuddin, Nurul Amelina A weighted difference loss approach for enhancing multi-label classification |
| title | A weighted difference loss approach for enhancing multi-label classification |
| title_full | A weighted difference loss approach for enhancing multi-label classification |
| title_fullStr | A weighted difference loss approach for enhancing multi-label classification |
| title_full_unstemmed | A weighted difference loss approach for enhancing multi-label classification |
| title_short | A weighted difference loss approach for enhancing multi-label classification |
| title_sort | weighted difference loss approach for enhancing multi-label classification |
| url | http://psasir.upm.edu.my/id/eprint/120145/ http://psasir.upm.edu.my/id/eprint/120145/ http://psasir.upm.edu.my/id/eprint/120145/ http://psasir.upm.edu.my/id/eprint/120145/1/120145.pdf |