ML-Plan: Automated Machine Learning via Hierarchical Planning
Abstract
Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches.
Cite
Text
Mohr et al. "ML-Plan: Automated Machine Learning via Hierarchical Planning." Machine Learning, 2018. doi:10.1007/S10994-018-5735-ZMarkdown
[Mohr et al. "ML-Plan: Automated Machine Learning via Hierarchical Planning." Machine Learning, 2018.](https://mlanthology.org/mlj/2018/mohr2018mlj-mlplan/) doi:10.1007/S10994-018-5735-ZBibTeX
@article{mohr2018mlj-mlplan,
title = {{ML-Plan: Automated Machine Learning via Hierarchical Planning}},
author = {Mohr, Felix and Wever, Marcel and Hüllermeier, Eyke},
journal = {Machine Learning},
year = {2018},
pages = {1495-1515},
doi = {10.1007/S10994-018-5735-Z},
volume = {107},
url = {https://mlanthology.org/mlj/2018/mohr2018mlj-mlplan/}
}