  Automatic machine learning (AutoML) is an area of research aimed at
automating machine learning (ML) activities that currently require human
experts. One of the most challenging tasks in this field is the automatic
generation of end-to-end ML pipelines: combining multiple types of ML
algorithms into a single architecture used for end-to-end analysis of
previously-unseen data. This task has two challenging aspects: the first is the
need to explore a large search space of algorithms and pipeline architectures.
The second challenge is the computational cost of training and evaluating
multiple pipelines. In this study we present DeepLine, a reinforcement learning
based approach for automatic pipeline generation. Our proposed approach
utilizes an efficient representation of the search space and leverages past
knowledge gained from previously-analyzed datasets to make the problem more
tractable. Additionally, we propose a novel hierarchical-actions algorithm that
serves as a plugin, mediating the environment-agent interaction in deep
reinforcement learning problems. The plugin significantly speeds up the
training process of our model. Evaluation on 56 datasets shows that DeepLine
outperforms state-of-the-art approaches both in accuracy and in computational
cost.
