autoBagging：学习使用元学习对装袋工作流进行排序
autoBagging: Learning to Rank Bagging Workflows with Metalearning
摘要：
机器学习（ML）已经成功地应用于广泛的领域和应用。大多数成功应用背后的技术之一是包围学习（EL），这是ML领域，催生了随机森林或Boosting等方法。应用这些技术的复杂性以及ML专家的市场稀缺性，创造了对能够快速轻松地替代ML库的系统的需求。自动化机器学习（autoML）是试图满足这些需求的ML领域。通常，这些系统依赖于诸如贝叶斯优化的优化技术来引导对最佳模型的搜索。我们的方法不同于这些系统，利用最新的进展元学习和学习排名的方法来学习元数据。我们提出了autoBagging，一个autoML系统，通过利用过去的性能和数据集特征，自动对63个装袋工作流进行排名。在OpenML平台的140个分类数据集上的结果表明，autoBagging可以产生比Average Rank方法更好的性能，并且与为每个数据集系统地选择最佳工作流的理想模型相比，其结果在统计上没有差异。出于可重复性和可推广性的目的，autoBagging在CRAN上作为R包公开提供。
英文摘要：
Machine Learning (ML) has been successfully applied to a wide range of domains and applications. One of the techniques behind most of these successful applications is Ensemble Learning (EL), the field of ML that gave birth to methods such as Random Forests or Boosting. The complexity of applying these techniques together with the market scarcity on ML experts, has created the need for systems that enable a fast and easy drop-in replacement for ML libraries. Automated machine learning (autoML) is the field of ML that attempts to answers these needs. Typically, these systems rely on optimization techniques such as bayesian optimization to lead the search for the best model. Our approach differs from these systems by making use of the most recent advances on metalearning and a learning to rank approach to learn from metadata. We propose autoBagging, an autoML system that automatically ranks 63 bagging workflows by exploiting past performance and dataset characterization. Results on 140 classification datasets from the OpenML platform show that autoBagging can yield better performance than the Average Rank method and achieve results that are not statistically different from an ideal model that systematically selects the best workflow for each dataset. For the purpose of reproducibility and generalizability, autoBagging is publicly available as an R package on CRAN.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
