IA-KNNR: A Novel Imbalance-Aware Approach for Handling Multi-Label Class Imbalance Problem
Multi-label learning (MLL) is a supervised learning where the classifier needs to learn from the data where one instance can belong to more than one class (label). Due to its wide application, MLL has attracted great attention from academia and researchers. However, due to the complex nature of mult...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11075775/ |
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| author | Himanshu Suyal Shiv Naresh Shivhare Gulshan Shrivastava Rohit Singh Ansh Singhal |
| author_facet | Himanshu Suyal Shiv Naresh Shivhare Gulshan Shrivastava Rohit Singh Ansh Singhal |
| author_sort | Himanshu Suyal |
| collection | DOAJ |
| description | Multi-label learning (MLL) is a supervised learning where the classifier needs to learn from the data where one instance can belong to more than one class (label). Due to its wide application, MLL has attracted great attention from academia and researchers. However, due to the complex nature of multi-label data (MLD), it suffers from the class imbalance problem, where data distribution is heterogeneous, and learning from that imbalanced data is challenging. To address this, we propose IA-KNNR, a novel imbalance-aware resampling framework that leverages distance-weighted K-nearest neighbors, label similarity, and a dynamic weighting strategy that accounts for the co-occurrence patterns between frequent and infrequent labels. In addition, DBSCAN-based prototype selection is used to select the most appropriate training data to reduce the complexity of the training time. Extensive experiments on ten multi-label benchmark datasets demonstrate that IA-KNNR performs significantly better than state-of-the-art approaches, up to 26.4%. |
| format | Article |
| id | doaj-art-954736c51a3b482ab23f6db18e79fd1c |
| institution | Matheson Library |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-954736c51a3b482ab23f6db18e79fd1c2025-07-17T23:01:53ZengIEEEIEEE Access2169-35362025-01-011311999912001710.1109/ACCESS.2025.358614611075775IA-KNNR: A Novel Imbalance-Aware Approach for Handling Multi-Label Class Imbalance ProblemHimanshu Suyal0https://orcid.org/0000-0002-8731-5800Shiv Naresh Shivhare1https://orcid.org/0000-0002-6306-1113Gulshan Shrivastava2https://orcid.org/0000-0003-3671-4921Rohit Singh3https://orcid.org/0000-0002-7626-8885Ansh Singhal4https://orcid.org/0009-0008-0922-8200School of Computer Science Engineering and Technology, Bennett University, Greater Noida, Uttar Pradesh, IndiaSchool of Computer Science Engineering and Technology, Bennett University, Greater Noida, Uttar Pradesh, IndiaSchool of Computer Science Engineering and Technology, Bennett University, Greater Noida, Uttar Pradesh, IndiaSchool of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, IndiaSchool of Computer Science Engineering and Technology, Bennett University, Greater Noida, Uttar Pradesh, IndiaMulti-label learning (MLL) is a supervised learning where the classifier needs to learn from the data where one instance can belong to more than one class (label). Due to its wide application, MLL has attracted great attention from academia and researchers. However, due to the complex nature of multi-label data (MLD), it suffers from the class imbalance problem, where data distribution is heterogeneous, and learning from that imbalanced data is challenging. To address this, we propose IA-KNNR, a novel imbalance-aware resampling framework that leverages distance-weighted K-nearest neighbors, label similarity, and a dynamic weighting strategy that accounts for the co-occurrence patterns between frequent and infrequent labels. In addition, DBSCAN-based prototype selection is used to select the most appropriate training data to reduce the complexity of the training time. Extensive experiments on ten multi-label benchmark datasets demonstrate that IA-KNNR performs significantly better than state-of-the-art approaches, up to 26.4%.https://ieeexplore.ieee.org/document/11075775/Multi-label classificationimbalanced learningoversamplingsustainable innovation |
| spellingShingle | Himanshu Suyal Shiv Naresh Shivhare Gulshan Shrivastava Rohit Singh Ansh Singhal IA-KNNR: A Novel Imbalance-Aware Approach for Handling Multi-Label Class Imbalance Problem IEEE Access Multi-label classification imbalanced learning oversampling sustainable innovation |
| title | IA-KNNR: A Novel Imbalance-Aware Approach for Handling Multi-Label Class Imbalance Problem |
| title_full | IA-KNNR: A Novel Imbalance-Aware Approach for Handling Multi-Label Class Imbalance Problem |
| title_fullStr | IA-KNNR: A Novel Imbalance-Aware Approach for Handling Multi-Label Class Imbalance Problem |
| title_full_unstemmed | IA-KNNR: A Novel Imbalance-Aware Approach for Handling Multi-Label Class Imbalance Problem |
| title_short | IA-KNNR: A Novel Imbalance-Aware Approach for Handling Multi-Label Class Imbalance Problem |
| title_sort | ia knnr a novel imbalance aware approach for handling multi label class imbalance problem |
| topic | Multi-label classification imbalanced learning oversampling sustainable innovation |
| url | https://ieeexplore.ieee.org/document/11075775/ |
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