The effectiveness of the machine learning methods for real-world tasks depends on the proper structure of the modeling pipeline. The proposed approach is aimed to automate the design of composite machine learning pipelines, which is equivalent to computation workflows that consist of models and data operations. The approach combines key ideas of both automated machine learning and workflow management systems. It designs the pipelines with a customizable graph-based structure, analyzes the obtained results, and reproduces them. The evolutionary approach is used for the flexible identification of pipeline structure. The additional algorithms for sensitivity analysis, atomization, and hyperparameter tuning are implemented to improve the effectiveness of the approach. Also, the software implementation on this approach is presented as an open-source framework. The set of experiments is conducted for the different datasets and tasks (classification, regression, time series forecasting). The obtained results confirm the correctness and effectiveness of the proposed approach in the comparison with the state-of-the-art competitors and baseline solutions.

机器学习方法对现实世界任务的有效性取决于建模管道的正确结构。所提出的方法旨在使复合机器学习管道的设计自动化，这相当于由模型和数据操作组成的计算工作流。该方法结合了自动化机器学习和工作流管理系统的关键思想。它使用可定制的基于图形的结构设计管道，分析获得的结果并重现它们。进化方法用于管道结构的灵活识别。实现了用于灵敏度分析、原子化和超参数调整的附加算法，以提高该方法的有效性。还，这种方法的软件实现是作为一个开源框架提供的。这组实验针对不同的数据集和任务（分类、回归、时间序列预测）进行。与最先进的竞争对手和基线解决方案相比，获得的结果证实了所提出的方法的正确性和有效性。