论文标题
MESA：通过元采样器增强合奏不平衡学习
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler
论文作者
论文摘要
不平衡的学习（IL），即从类失去平衡的数据中学习无偏见的模型，是一个具有挑战性的问题。典型的IL方法包括重新采样和重新加权，是根据某些启发式假设设计的。他们经常遭受不稳定的性能，差的适用性和高度计算成本，在他们的假设不存在的复杂任务中。在本文中，我们介绍了一个名为Mesa的新颖合奏IL框架。它可以适应迭代中的训练设置，以获取多个分类器并形成级联合奏模型。 MESA直接从数据中学习采样策略，以优化最终指标，而不是随机启发式方法。此外，与盛行的基于元学习的IL解决方案不同，我们通过独立训练元采样器对任务无关的元数据训练MESA中的模型训练和元训练。这使得MESA通常适用于大多数现有的学习模型，并且可以有效地应用于新任务。对合成和现实世界任务的广泛实验证明了MESA的有效性，鲁棒性和可转移性。我们的代码可从https://github.com/zhiningliu1998/mesa获得。
Imbalanced learning (IL), i.e., learning unbiased models from class-imbalanced data, is a challenging problem. Typical IL methods including resampling and reweighting were designed based on some heuristic assumptions. They often suffer from unstable performance, poor applicability, and high computational cost in complex tasks where their assumptions do not hold. In this paper, we introduce a novel ensemble IL framework named MESA. It adaptively resamples the training set in iterations to get multiple classifiers and forms a cascade ensemble model. MESA directly learns the sampling strategy from data to optimize the final metric beyond following random heuristics. Moreover, unlike prevailing meta-learning-based IL solutions, we decouple the model-training and meta-training in MESA by independently train the meta-sampler over task-agnostic meta-data. This makes MESA generally applicable to most of the existing learning models and the meta-sampler can be efficiently applied to new tasks. Extensive experiments on both synthetic and real-world tasks demonstrate the effectiveness, robustness, and transferability of MESA. Our code is available at https://github.com/ZhiningLiu1998/mesa.
