A weighted difference loss approach for enhancing multi-label classification
Abstract 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 nuance...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-09883-2 |
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| author | Qiong Hu Masrah Azrifah Azmi Murad Azreen Bin Azman Nurul Amelina Nasharuddin |
| author_facet | Qiong Hu Masrah Azrifah Azmi Murad Azreen Bin Azman Nurul Amelina Nasharuddin |
| author_sort | Qiong Hu |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-cc1c3365be8445b3aec86d9b2d66a2b9 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-cc1c3365be8445b3aec86d9b2d66a2b92025-08-20T03:45:53ZengNature PortfolioScientific Reports2045-23222025-07-0115111310.1038/s41598-025-09883-2A weighted difference loss approach for enhancing multi-label classificationQiong Hu0Masrah Azrifah Azmi Murad1Azreen Bin Azman2Nurul Amelina Nasharuddin3Faculty of Computer Science and Information Technology, UPM Lebuh UniversitiFaculty of Computer Science and Information Technology, UPM Lebuh UniversitiFaculty of Computer Science and Information Technology, UPM Lebuh UniversitiFaculty of Computer Science and Information Technology, UPM Lebuh UniversitiAbstract 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.https://doi.org/10.1038/s41598-025-09883-2Multi-label Sentiment ClassificationBERTLoss Function OptimizationLabel Dependency ModelingWeighted Difference Loss (WDL) |
| spellingShingle | Qiong Hu Masrah Azrifah Azmi Murad Azreen Bin Azman Nurul Amelina Nasharuddin A weighted difference loss approach for enhancing multi-label classification Scientific Reports Multi-label Sentiment Classification BERT Loss Function Optimization Label Dependency Modeling Weighted Difference Loss (WDL) |
| 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 |
| topic | Multi-label Sentiment Classification BERT Loss Function Optimization Label Dependency Modeling Weighted Difference Loss (WDL) |
| url | https://doi.org/10.1038/s41598-025-09883-2 |
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