Collaborative Boosting for Imbalanced Multiclass Learning
Analysis
This paper addresses the challenge of class imbalance in multiclass classification, a common problem in machine learning. It proposes a novel boosting model that collaboratively optimizes imbalanced learning and model training. The key innovation lies in integrating density and confidence factors, along with a noise-resistant weight update and dynamic sampling strategy. The collaborative approach, where these components work together, is the core contribution. The paper's significance is supported by the claim of outperforming state-of-the-art baselines on a range of datasets.
Key Takeaways
- •Proposes a novel boosting model for multiclass imbalanced learning.
- •Employs a collaborative optimization approach integrating density and confidence factors.
- •Features a noise-resistant weight update mechanism and dynamic sampling.
- •Demonstrates superior performance compared to state-of-the-art baselines.
- •Code is publicly available.
“The paper's core contribution is the collaborative optimization of imbalanced learning and model training through the integration of density and confidence factors, a noise-resistant weight update mechanism, and a dynamic sampling strategy.”