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No Free Lunch in imbalanced learning
Knowledge-Based Systems ( IF 8.8 ) Pub Date : 2021-06-12 , DOI: 10.1016/j.knosys.2021.107222
Nuno Moniz , Hugo Monteiro

The No Free Lunch (NFL) theorems have sparked intense debate since their publication, from theoretical and practical perspectives. However, to this date, no discussion is provided concerning its impact in the established field of imbalanced domain learning (IDL), known for its challenges regarding learning and evaluation processes. Most importantly, understanding the effect of commonly used solutions in such a field would prove very useful for future research. In this paper, we study the impact of data pre-processing methods, also known as resampling strategies, under the framework of the NFL theorems. Focusing on binary classification tasks, we claim that in IDL settings, when given a learning algorithm and a uniformly distributed set of target functions, the core conclusions of the NFL theorems are extensible to resampling strategies. As such, given no a priori knowledge or assumptions concerning data domains, any two resampling strategies are identical concerning their impact in the performance of predictive models. We provide a theoretical analysis and discussion on the intersection between IDL and the NFL theorems to support such a claim. Also, we collect empirical evidence via a thorough experimental study, including 98 data sets from multiple real-world knowledge domains.



中文翻译:

不平衡学习中没有免费午餐

从理论和实践的角度来看,无免费午餐 (NFL) 定理自发表以来就引发了激烈的争论。然而,到目前为止,尚未讨论其在不平衡域学习 (IDL) 的既定领域的影响,该领域以其在学习和评估过程方面的挑战而闻名。最重要的是,了解此类领域中常用解决方案的影响将证明对未来的研究非常有用。在本文中,我们在 NFL 定理的框架下研究了数据预处理方法(也称为重采样策略)的影响。专注于二元分类任务,我们声称在 IDL 设置中,当给定学习算法和均匀分布的目标函数集时,NFL 定理的核心结论可扩展到重采样策略。因此,鉴于没有关于数据域的先验知识或假设,任何两种重采样策略在对预测模型性能的影响方面都是相同的。我们对 IDL 和 NFL 定理之间的交集进行了理论分析和讨论,以支持这种说法。此外,我们通过彻底的实验研究收集了经验证据,包括来自多个现实世界知识领域的 98 个数据集。

更新日期:2021-06-18
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