- author = "Teddy Lazebnik and Amit Somech and Abraham Itzhak Weinberg",
- title = "{SubStrat}: A Subset-Based Optimization Strategy for Faster {AutoML}",
- journal = "Proceedings of the VLDB Endowment",
- year = "2022",
- volume = "16",
- number = "4",
- pages = "772--780",
- month = dec,
- keywords = "genetic algorithms, genetic programming, TPOT",
- publisher = "VLDB Endowment",
- ISSN = "2150-8097",
-
DOI = "
doi:10.14778/3574245.3574261",
- size = "9 pages",
-
abstract = "Automated machine learning (AutoML) frameworks have
become important tools in the data scientist arsenal,
as they dramatically reduce the manual work devoted to
the construction of ML pipelines. Such frameworks
intelligently search among millions of possible ML
pipelines, typically containing feature engineering,
model selection, and hyper parameters tuning steps, and
finally output an optimal pipeline in terms of
predictive accuracy.
However, when the dataset is large, each individual configuration takes longer to execute, therefore the overall AutoML running times become increasingly high.
To this end, we present SubStrat, an AutoML optimization strategy that tackles the data size, rather than configuration space. It wraps existing AutoML tools, and instead of executing them directly on the entire dataset, SubStrat uses a genetic-based algorithm to find a small yet representative data subset that preserves a particular characteristic of the full data. It then employs the AutoML tool on the small subset, and finally, it refines the resulting pipeline by executing a restricted, much shorter, AutoML process on the large dataset. Our experimental results, performed on three popular AutoML frameworks, Auto-Sklearn, TPOT, and H2O show that SubStrat reduces their running times by 76.3 percent (on average), with only a 4.15 percent average decrease in the accuracy of the resulting ML pipeline.",
