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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.07245 |
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| _version_ | 1866909607601897472 |
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| author | Liu, Fei Ren, Huanhuan Guan, Yu Wang, Xiuxu Lv, Wang Hu, Zhiqiang Chen, Yaxi |
| author_facet | Liu, Fei Ren, Huanhuan Guan, Yu Wang, Xiuxu Lv, Wang Hu, Zhiqiang Chen, Yaxi |
| contents | Predicting future vehicle purchases among existing owners presents a critical challenge due to extreme class imbalance (<0.5% positive rate) and complex behavioral patterns. We propose REMEDI (Relative feature Enhanced Meta-learning with Distillation for Imbalanced prediction), a novel multi-stage framework addressing these challenges. REMEDI first trains diverse base models to capture complementary aspects of user behavior. Second, inspired by comparative op-timization techniques, we introduce relative performance meta-features (deviation from ensemble mean, rank among peers) for effective model fusion through a hybrid-expert architecture. Third, we distill the ensemble's knowledge into a single efficient model via supervised fine-tuning with MSE loss, enabling practical deployment. Evaluated on approximately 800,000 vehicle owners, REMEDI significantly outperforms baseline approaches, achieving the business target of identifying ~50% of actual buyers within the top 60,000 recommendations at ~10% precision. The distilled model preserves the ensemble's predictive power while maintaining deployment efficiency, demonstrating REMEDI's effectiveness for imbalanced prediction in industry settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07245 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | REMEDI: Relative Feature Enhanced Meta-Learning with Distillation for Imbalanced Prediction Liu, Fei Ren, Huanhuan Guan, Yu Wang, Xiuxu Lv, Wang Hu, Zhiqiang Chen, Yaxi Machine Learning Artificial Intelligence Predicting future vehicle purchases among existing owners presents a critical challenge due to extreme class imbalance (<0.5% positive rate) and complex behavioral patterns. We propose REMEDI (Relative feature Enhanced Meta-learning with Distillation for Imbalanced prediction), a novel multi-stage framework addressing these challenges. REMEDI first trains diverse base models to capture complementary aspects of user behavior. Second, inspired by comparative op-timization techniques, we introduce relative performance meta-features (deviation from ensemble mean, rank among peers) for effective model fusion through a hybrid-expert architecture. Third, we distill the ensemble's knowledge into a single efficient model via supervised fine-tuning with MSE loss, enabling practical deployment. Evaluated on approximately 800,000 vehicle owners, REMEDI significantly outperforms baseline approaches, achieving the business target of identifying ~50% of actual buyers within the top 60,000 recommendations at ~10% precision. The distilled model preserves the ensemble's predictive power while maintaining deployment efficiency, demonstrating REMEDI's effectiveness for imbalanced prediction in industry settings. |
| title | REMEDI: Relative Feature Enhanced Meta-Learning with Distillation for Imbalanced Prediction |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2505.07245 |