[论文解读] A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation

Azal Ahmad Khan, Omkar Chaudhari+1 more|arXiv (Cornell University)|Apr 6, 2023

Imbalanced Data Classification Techniques被引用 21

一句话总结

该论文进行了一项计算研究，评估9种数据增强和9种集成学习方法在23个二分类不平衡数据集上的效果，确定有效组合，并指出SMOTE/ROS在准确性和效率上常常优于基于GAN的增强方法。

ABSTRACT

Class imbalance (CI) in classification problems arises when the number of observations belonging to one class is lower than the other. Ensemble learning combines multiple models to obtain a robust model and has been prominently used with data augmentation methods to address class imbalance problems. In the last decade, a number of strategies have been added to enhance ensemble learning and data augmentation methods, along with new methods such as generative adversarial networks (GANs). A combination of these has been applied in many studies, and the evaluation of different combinations would enable a better understanding and guidance for different application domains. In this paper, we present a computational study to evaluate data augmentation and ensemble learning methods used to address prominent benchmark CI problems. We present a general framework that evaluates 9 data augmentation and 9 ensemble learning methods for CI problems. Our objective is to identify the most effective combination for improving classification performance on imbalanced datasets. The results indicate that combinations of data augmentation methods with ensemble learning can significantly improve classification performance on imbalanced datasets. We find that traditional data augmentation methods such as the synthetic minority oversampling technique (SMOTE) and random oversampling (ROS) are not only better in performance for selected CI problems, but also computationally less expensive than GANs. Our study is vital for the development of novel models for handling imbalanced datasets.

研究动机与目标

• 评估数据增强和集成学习如何结合以提升不平衡分类任务的性能。
• 在有效性和效率方面比较经典的与基于GAN的增强技术。
• 为在基准数据集上评估 CI 方法提供通用框架和开源资源。

提出的方法

• 提出一个通用框架，在23个具有不同不平衡比率的二分类 CI 数据集上评估9种数据增强方法和9种集成学习方法。
• 回顾并总结集成学习方法：Bagging、Boosting和Stacking，以及代表性算法（如随机森林、AdaBoost、梯度提升、XGBoost、LightGBM）。
• 调查数据增强技术，重点关注基于SMOTE的方法及其变体（如 SMOTE-ENN）。
• 提供核心方法的关键方程和概念性描述（如 AdaBoost 加权、梯度提升残差、带正则化的 XGBoost 损失）。
• 讨论在将增强与集成结合时的评估指标和计算考量。

(a) Keyword search: “Class Imbalance.”

实验结果

研究问题

• RQ1 哪些数据增强与集成学习的组合在突出CI基准问题上能实现最佳性能？
• RQ2 在CI任务中，基于SMOTE的增强方法在准确性和计算成本方面与基于GAN的增强方法相比如何？
• RQ3 可以为在不同领域和不同不平衡比下选择增强-集成组合提供哪些指导？

主要发现

• 数据增强方法与集成学习的组合可以显著提升不平衡数据集上的分类性能。
• 传统的增强方法如 SMOTE 和随机过采样（ROS）在某些 CI 问题上可以优于 GAN 基方法，且计算成本更低。
• 本文提供了一个开源框架、代码和数据，支持社区对 CI 方法的评估。

(b) Keyword search: “Ensemble Learning.”