GAT-RWOS: Graph Attention-Guided Random Walk Oversampling for Imbalanced Data Classification
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913622635053056 |
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| author | Rustamov, Zahiriddin Lakas, Abderrahmane Zaki, Nazar |
| author_facet | Rustamov, Zahiriddin Lakas, Abderrahmane Zaki, Nazar |
| contents | Class imbalance poses a significant challenge in machine learning (ML), often leading to biased models favouring the majority class. In this paper, we propose GAT-RWOS, a novel graph-based oversampling method that combines the strengths of Graph Attention Networks (GATs) and random walk-based oversampling. GAT-RWOS leverages the attention mechanism of GATs to guide the random walk process, focusing on the most informative neighbourhoods for each minority node. By performing attention-guided random walks and interpolating features along the traversed paths, GAT-RWOS generates synthetic minority samples that expand class boundaries while preserving the original data distribution. Extensive experiments on a diverse set of imbalanced datasets demonstrate the effectiveness of GAT-RWOS in improving classification performance, outperforming state-of-the-art oversampling techniques. The proposed method has the potential to significantly improve the performance of ML models on imbalanced datasets and contribute to the development of more reliable classification systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_16394 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GAT-RWOS: Graph Attention-Guided Random Walk Oversampling for Imbalanced Data Classification Rustamov, Zahiriddin Lakas, Abderrahmane Zaki, Nazar Machine Learning Class imbalance poses a significant challenge in machine learning (ML), often leading to biased models favouring the majority class. In this paper, we propose GAT-RWOS, a novel graph-based oversampling method that combines the strengths of Graph Attention Networks (GATs) and random walk-based oversampling. GAT-RWOS leverages the attention mechanism of GATs to guide the random walk process, focusing on the most informative neighbourhoods for each minority node. By performing attention-guided random walks and interpolating features along the traversed paths, GAT-RWOS generates synthetic minority samples that expand class boundaries while preserving the original data distribution. Extensive experiments on a diverse set of imbalanced datasets demonstrate the effectiveness of GAT-RWOS in improving classification performance, outperforming state-of-the-art oversampling techniques. The proposed method has the potential to significantly improve the performance of ML models on imbalanced datasets and contribute to the development of more reliable classification systems. |
| title | GAT-RWOS: Graph Attention-Guided Random Walk Oversampling for Imbalanced Data Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2412.16394 |