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.

翻译：本文提出了一种用于流水线与深层特征合成的知识驱动型AutoML架构。其主要目标是使AutoML过程具备可解释性，并在流水线与特征合成过程中利用领域知识。该架构探索了若干创新思路：首先，以统一的方式处理流水线和深层特征的构建；其次，合成过程由一个共享知识系统驱动，该系统以交互式查询的方式确定应使用的流水线操作或需计算的特征；最后，该合成过程在运行时依据局部解决方案及其在数据上的应用结果做出决策。通过两组实验，本文展示了所提架构一个朴素实现的功能，并讨论了其优势、权衡以及AutoML领域的未来潜力。