Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization

M.D. Wever, F. Mohr, E. Hüllermeier, in: 27th Workshop Computational Intelligence, Dortmund, 2017.

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Abstract
These days, there is a strong rise in the needs for machine learning applications, requiring an automation of machine learning engineering which is referred to as AutoML. In AutoML the selection, composition and parametrization of machine learning algorithms is automated and tailored to a specific problem, resulting in a machine learning pipeline. Current approaches reduce the AutoML problem to optimization of hyperparameters. Based on recursive task networks, in this paper we present one approach from the field of automated planning and one evolutionary optimization approach. Instead of simply parametrizing a given pipeline, this allows for structure optimization of machine learning pipelines, as well. We evaluate the two approaches in an extensive evaluation, finding both approaches to have their strengths in different areas. Moreover, the two approaches outperform the state-of-the-art tool Auto-WEKA in many settings.
Publishing Year
Proceedings Title
27th Workshop Computational Intelligence
Conference
27th Workshop Computational Intelligence
Conference Location
Dortmund
Conference Date
2017-11-23 – 2017-11-24
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Wever MD, Mohr F, Hüllermeier E. Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. In: 27th Workshop Computational Intelligence. Dortmund; 2017.
Wever, M. D., Mohr, F., & Hüllermeier, E. (2017). Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. In 27th Workshop Computational Intelligence. Dortmund.
@inproceedings{Wever_Mohr_Hüllermeier_2017, place={Dortmund}, title={Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization}, booktitle={27th Workshop Computational Intelligence}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2017} }
Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization.” In 27th Workshop Computational Intelligence. Dortmund, 2017.
M. D. Wever, F. Mohr, and E. Hüllermeier, “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization,” in 27th Workshop Computational Intelligence, Dortmund, 2017.
Wever, Marcel Dominik, et al. “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization.” 27th Workshop Computational Intelligence, 2017.
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