整合学习--一种特定领域的无模型优化策略，并以XGBoost和MIMIC- IV为例
Consolidated learning -- a domain-specific model-free optimization strategy with examples for XGBoost and MIMIC-IV
摘要：
对于许多机器学习模型，超参数的选择是实现高性能的关键一步。流行的元学习方法专注于基于从先前任务获得的结果，为全新的任务获得具有有限计算预算的良好超参数配置。本文提出了一种新的配方的调整问题，称为合并学习，更适合于模型开发人员所面临的实际挑战，其中大量的预测模型创建在类似的数据集。在这样的设置中，我们感兴趣的是总的优化时间，而不是针对单个任务进行调优。我们表明，一个精心挑选的静态组合的超参数产生良好的效果，随时优化，保持易用性和实施。此外，我们指出如何构建这样的投资组合的特定领域。优化的改进是可能的，因为类似任务之间的超参数配置的更有效的传输。我们通过对XGBoost算法的实证研究和从MIMIC-IV医学数据库中提取的预测任务集合来证明这种方法的有效性;然而，合并学习适用于许多其他领域。
英文摘要：
For many machine learning models, a choice of hyperparameters is a crucial step towards achieving high performance. Prevalent meta-learning approaches focus on obtaining good hyperparameters configurations with a limited computational budget for a completely new task based on the results obtained from the prior tasks. This paper proposes a new formulation of the tuning problem, called consolidated learning, more suited to practical challenges faced by model developers, in which a large number of predictive models are created on similar data sets. In such settings, we are interested in the total optimization time rather than tuning for a single task. We show that a carefully selected static portfolio of hyperparameters yields good results for anytime optimization, maintaining ease of use and implementation. Moreover, we point out how to construct such a portfolio for specific domains. The improvement in the optimization is possible due to more efficient transfer of hyperparameter configurations between similar tasks. We demonstrate the effectiveness of this approach through an empirical study for XGBoost algorithm and the collection of predictive tasks extracted from the MIMIC-IV medical database; however, consolidated learning is applicable in many others fields.
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。
