SubStrat：一种基于数据子集的加速AutoML策略
SubStrat: A Subset-Based Strategy for Faster AutoML
AI总结：
SubStrat提出一种基于遗传算法选取代表性数据子集来加速AutoML的策略，通过先在小子集上搜索、再在大数据集上受限优化，在Auto-Sklearn和TPOT上平均减少79%运行时间且精度损失不到2%。
摘要：
自动化机器学习（AutoML）框架已成为数据科学家工具箱中的重要工具，因为它们大幅减少了构建机器学习流水线所需的人工工作。这类框架在数百万种可能的机器学习流水线中智能搜索——通常包含特征工程、模型选择和超参数调优步骤——并最终输出一个在预测精度方面最优的流水线。然而，当数据集规模较大时，每个单独配置的执行时间都会变长，因此AutoML的整体运行时间会变得非常高。为此，我们提出了SubStrat，这是一种AutoML优化策略，它解决的是数据规模问题，而非配置空间问题。SubStrat封装现有的AutoML工具，不是直接在整个数据集上执行它们，而是使用基于遗传的算法来寻找一个规模小但具有代表性的数据子集，该子集保留了完整数据的特定特征。然后，它在小型子集上使用AutoML工具，最后，通过在大数据集上执行一个受限的、短得多的AutoML过程来优化所得到的流水线。我们在两个流行的AutoML框架Auto-Sklearn和TPOT上进行的实验结果表明，SubStrat将其运行时间平均减少了79%，而所得机器学习流水线的精度平均损失不到2%。
英文摘要：
Automated machine learning (AutoML) frameworks have become important tools in the data scientists' arsenal, as they dramatically reduce the manual work devoted to the construction of ML pipelines. Such frameworks intelligently search among millions of possible ML pipelines - typically containing feature engineering, model selection and hyper parameters tuning steps - and finally output an optimal pipeline in terms of predictive accuracy. However, when the dataset is large, each individual configuration takes longer to execute, therefore the overall AutoML running times become increasingly high. To this end, we present SubStrat, an AutoML optimization strategy that tackles the data size, rather than configuration space. It wraps existing AutoML tools, and instead of executing them directly on the entire dataset, SubStrat uses a genetic-based algorithm to find a small yet representative data subset which preserves a particular characteristic of the full data. It then employs the AutoML tool on the small subset, and finally, it refines the resulted pipeline by executing a restricted, much shorter, AutoML process on the large dataset. Our experimental results, performed on two popular AutoML frameworks, Auto-Sklearn and TPOT, show that SubStrat reduces their running times by 79% (on average), with less than 2% average loss in the accuracy of the resulted ML pipeline.
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。
