论文标题
深度多目标预测中的超参数优化
Hyperparameter optimization in deep multi-target prediction
论文作者
论文摘要
由于配置和微调的机器学习模型的复杂性越来越不断增加，自动化机器学习（AUTOML）的领域已经出现在过去十年中。但是，诸如Auto-Weka和Auto-Sklearn之类的软件实现通常专注于古典机器学习（ML）任务，例如分类和回归。我们的工作可以看作是为大多数问题设置提供单个汽车框架的首次尝试，这些设置属于多目标预测的范围，其中包括流行的ML设置，例如多标签分类，多变量回归，多任务研究，多任务学习，多任务学习，二元预测，Matrix完成，Matrix完成和零量学习。自动化问题选择和模型配置是通过扩展DeepMtp（MTP问题设置的一般深度学习框架，具有流行的超参数优化（HPO）方法）来实现的。我们在不同数据集和MTP问题设置上进行的广泛基准测试确定了特定HPO方法优于其他方法的情况。
As a result of the ever increasing complexity of configuring and fine-tuning machine learning models, the field of automated machine learning (AutoML) has emerged over the past decade. However, software implementations like Auto-WEKA and Auto-sklearn typically focus on classical machine learning (ML) tasks such as classification and regression. Our work can be seen as the first attempt at offering a single AutoML framework for most problem settings that fall under the umbrella of multi-target prediction, which includes popular ML settings such as multi-label classification, multivariate regression, multi-task learning, dyadic prediction, matrix completion, and zero-shot learning. Automated problem selection and model configuration are achieved by extending DeepMTP, a general deep learning framework for MTP problem settings, with popular hyperparameter optimization (HPO) methods. Our extensive benchmarking across different datasets and MTP problem settings identifies cases where specific HPO methods outperform others.
