Automatic Frankensteining: Creating Complex Ensembles Autonomously

By Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme. 2017

33 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Benchmark and Survey of Automated Machine Learning Frameworks (2019, background): “Instead of only yielding the best performing configuration, the set of best performing configurations can be used to create an ensemble (Lacoste et al., 2014; Feurer et al., 2015a; Wistuba et al., 2017).”
Survey on Automated Machine Learning (2019, positive): “Instead of only yielding the best performing configuration, the set of best performing configurations can be used to create an ensemble (Lacoste et al., 2014; Feurer et al., 2015a; Wistuba et al., 2017).”
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning (2020, methodology): “Instead of using a single layer of machine learning models, Automatic Frankensteining (Wistuba et al., 2017) proposed two-layer stacking, applying AutoML to the outputs of an AutoML system instead of a single layer of ML algorithms followed by an ensembling mechanism.”
Deep Ensembles for Low-Data Transfer Learning (2020, methodology): “, 2019) to explicitly optimise both the ensembling method and the members to maximise overall performance (Wistuba et al., 2017; Xavier-Júnior et al., 2018).”
One button machine for automating feature engineering in relational databases (2017, unknown): “nd conﬁguration for different analytics tasks and an interactive user interface. CADS is one of the ﬁrst solutions, that was deployed in industry. Besides the aforementioned works, Automatic Ensemble [12] is the most recent work which uses stacking and metadata to assist model selection and tuning. ”