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Mastering the Minority: An Uncertainty-guided Multi-Expert Framework for Challenging-tailed Sequence Learning

arXiv:2603.15708v1 Announce Type: new Abstract: Imbalanced data distribution remains a critical challenge in sequential learning, leading models to easily recognize frequent categories while failing to detect minority classes adequately. The Mixture-of-Experts model offers a scalable solution, yet its application is often hindered by parameter inefficiency, poor expert specialization, and difficulty in resolving prediction conflicts. To Master the Minority classes effectively, we propose the Uncertainty-based Multi-Expert fusion network (UME) framework. UME is designed with three core innovations: First, we employ Ensemble LoRA for parameter-efficient modeling, significantly reducing the trainable parameter count. Second, we introduce Sequential Specialization guided by Dempster-Shafer Theory (DST), which ensures effective specialization on the challenging-tailed classes. Finally, an Uncertainty-Guided Fusion mechanism uses DST's certainty measures to dynamically weigh expert opinions

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Ye Wang, Zixuan Wu, Lifeng Shen, Jiang Xie, Xiaoling Wang, Hong Yu, Guoyin Wang · · 1 min read · 6 views
#cs.LG #cs.AI

arXiv:2603.15708v1 Announce Type: new Abstract: Imbalanced data distribution remains a critical challenge in sequential learning, leading models to easily recognize frequent categories while failing to detect minority classes adequately. The Mixture-of-Experts model offers a scalable solution, yet its application is often hindered by parameter inefficiency, poor expert specialization, and difficulty in resolving prediction conflicts. To Master the Minority classes effectively, we propose the Uncertainty-based Multi-Expert fusion network (UME) framework. UME is designed with three core innovations: First, we employ Ensemble LoRA for parameter-efficient modeling, significantly reducing the trainable parameter count. Second, we introduce Sequential Specialization guided by Dempster-Shafer Theory (DST), which ensures effective specialization on the challenging-tailed classes. Finally, an Uncertainty-Guided Fusion mechanism uses DST's certainty measures to dynamically weigh expert opinions, resolving conflicts by prioritizing the most confident expert for reliable final predictions. Extensive experiments across four public hierarchical text classification datasets demonstrate that UME achieves state-of-the-art performance. We achieve a performance gain of up to 17.97\% over the best baseline on individual categories, while reducing trainable parameters by up to 10.32\%. The findings highlight that uncertainty-guided expert coordination is a principled strategy for addressing challenging-tailed sequence learning. Our code is available at https://github.com/CQUPTWZX/Multi-experts.

Executive Summary

This study proposes the Uncertainty-based Multi-Expert fusion network (UME) framework to address the challenges of imbalanced data distribution in sequential learning. UME incorporates three core innovations: Ensemble LoRA for parameter-efficient modeling, Sequential Specialization guided by Dempster-Shafer Theory (DST), and Uncertainty-Guided Fusion. The framework is tested on four public hierarchical text classification datasets, achieving state-of-the-art performance and reducing trainable parameters by up to 10.32%. The findings demonstrate the effectiveness of uncertainty-guided expert coordination in challenging-tailed sequence learning. The study's contributions lie in providing a principled strategy for addressing imbalanced data distribution and achieving significant performance gains. The proposed framework has the potential to be applied in various sequential learning tasks, including natural language processing and time-series forecasting.

Key Points

  • ▸ Proposes the Uncertainty-based Multi-Expert fusion network (UME) framework to address imbalanced data distribution
  • ▸ Incorporates three core innovations: Ensemble LoRA, Sequential Specialization, and Uncertainty-Guided Fusion
  • ▸ Achieves state-of-the-art performance on four public hierarchical text classification datasets

Merits

Strength of the Framework

The UME framework demonstrates significant performance gains over existing methods, achieving up to 17.97% improvement on individual categories. The framework's ability to reduce trainable parameters by up to 10.32% is also a notable advantage.

Demerits

Limitation of the Study

The study focuses primarily on hierarchical text classification datasets, and its applicability to other sequential learning tasks is unclear. Additionally, the framework's performance on more complex and diverse datasets remains to be explored.

Expert Commentary

The study's proposed framework is a significant contribution to the field of sequential learning, addressing a critical challenge in machine learning. The use of uncertainty-guided expert coordination is a principled strategy for addressing challenging-tailed sequence learning, with potential applications in various sequential learning tasks. However, the study's focus on hierarchical text classification datasets limits its generalizability. Future research should explore the framework's performance on more complex and diverse datasets. Additionally, the study's implications for policy and decision-making warrant further investigation.

Recommendations

  • ✓ Future research should explore the framework's performance on more complex and diverse datasets.
  • ✓ The study's proposed framework should be applied to various sequential learning tasks, including natural language processing and time-series forecasting.

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