论文标题
错误分类成本敏感的合奏学习：一个统一的框架
Misclassification cost-sensitive ensemble learning: A unifying framework
论文作者
论文摘要
多年来，当不同类型的错误分类错误产生不同的成本时，已经提出了多种成本敏感方法来学习数据。我们的贡献是一个统一的框架，可为成本敏感的合奏方法提供全面而有见地的概述，通过细粒度的分类来指出它们的差异和相似性。我们的框架包含跨方法的自然扩展和思想的概括，无论它是Adaboost，袋还是随机森林，因此不仅产生了迄今为止已知的所有方法，而且会产生一些以前未考虑的方法。
Over the years, a plethora of cost-sensitive methods have been proposed for learning on data when different types of misclassification errors incur different costs. Our contribution is a unifying framework that provides a comprehensive and insightful overview on cost-sensitive ensemble methods, pinpointing their differences and similarities via a fine-grained categorization. Our framework contains natural extensions and generalisations of ideas across methods, be it AdaBoost, Bagging or Random Forest, and as a result not only yields all methods known to date but also some not previously considered.
