This paper proposes a knowledge-driven AutoML architecture for pipeline and
deep feature synthesis. The main goal is to render the AutoML process
explainable and to leverage domain knowledge in the synthesis of pipelines and
features. The architecture explores several novel ideas: first, the
construction of pipelines and deep features is approached in an unified way.
Next, synthesis is driven by a shared knowledge system, interactively queried
as to what pipeline operations to use or features to compute. Lastly, the
synthesis processes takes decisions at runtime using partial solutions and
results of their application on data. Two experiments are conducted to
demonstrate the functionality of a na\"{\i}ve implementation of the proposed
architecture and to discuss its advantages, trade-offs as well as future
potential for AutoML.

该论文提出了一种以知识驱动的自动机器学习架构，用于管道和深度特征的合成，旨在使自动机器学习过程可解释，并在管道和特征的合成中利用领域知识。该架构探索了几个创新理念：首先，以统一的方式构建管道和深度特征；其次，通过一个共享的知识系统进行合成，可以交互式地查询要使用的管道操作或计算的特征；最后，合成过程在运行时使用部分解决方案和其在数据上应用的结果来进行决策。进行了两个实验来演示所提议的架构的基本实现功能，并讨论了其优势、权衡以及在自动机器学习中的未来潜力。