TY - JOUR AU - Marinescu, Radu AU - Kishimoto, Akihiro AU - Ram, Parikshit AU - Rawat, Ambrish AU - Wistuba, Martin AU - Palmes, Paulito P. AU - Botea, Adi PY - 2021/05/18 Y2 - 2024/03/28 TI - Searching for Machine Learning Pipelines Using a Context-Free Grammar JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 10 SE - AAAI Technical Track on Machine Learning III DO - 10.1609/aaai.v35i10.17077 UR - https://ojs.aaai.org/index.php/AAAI/article/view/17077 SP - 8902-8911 AB - AutoML automatically selects, composes and parameterizes machine learning algorithms into a workflow or pipeline of operations that aims at maximizing performance on a given dataset. Although current methods for AutoML achieved impressive results they mostly concentrate on optimizing fixed linear workflows. In this paper, we take a different approach and focus on generating and optimizing pipelines of complex directed acyclic graph shapes. These complex pipeline structure may lead to discovering hidden features and thus boost performance considerably. We explore the power of heuristic search and context-free grammars to search and optimize these kinds of pipelines. Experiments on various benchmark datasets show that our approach is highly competitive and often outperforms existing AutoML systems. ER -