RECIPE: A Grammar-Based Framework for Automatically Evolving Classification Pipelines

By A. D. Sá, Walter José G. S. Pinto, Luiz Otavio Vilas Boas Oliveira, Gisele Lobo Pappa. 2017

111 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Automated Machine Learning: State-of-The-Art and Open Challenges (2019, methodology): “Recipe [28] follows the same optimization procedure as TPOT using genetic programming, which in turn exploits the advantages of a global search.”
Benchmark and Survey of Automated Machine Learning Frameworks (2019, background): “To prevent such defective pipelines, the pipeline creation can be restricted by a grammar (de Sá et al., 2017; Drori et al., 2019).”
Survey on Automated Machine Learning (2019, neutral): “To prevent such defective pipelines, RECIPE (de Sá et al., 2017) has been proposed.”
Meta-Learning: A Survey (2018, neutral): “This is critical when computational resources are limited, and has proven to be an important factor in practical comparisons of AutoML systems [33].”
Auto-sklearn: Efficient and Robust Automated Machine Learning (2019, methodology): “Since the publication of the original NIPS paper [14] Auto-sklearn has become a standard baseline for new approaches to automated machine learning such as FLASH [39], RECIPE [32], Hyperband [25], Auto-Prognosis [2], ML-PLAN [28], Auto-Stacker [10] and AlphaD3M [11].”
Automated Selection and Configuration of Multi-Label Classification Algorithms with Grammar-Based Genetic Programming (2018, methodology): “Most Auto-ML systems proposed to date focus on generating sequences of steps to solve single label classification (SLC) problems [3, 5, 6, 8, 15].”
ML-Plan: Automated machine learning via hierarchical planning (2018, methodology): “In fact, RECIPE has so far only been evaluated on rather small datasets, and our evaluation shows that TPOT is not able to return any solution for the more difficult problems even within 1 day.”
Bandit-Based Automated Machine Learning (2018, methodology): “To select methods and algorithms that performs well for a given task it is usually required deep domain expertise, bruteforce and/or laborious hand-tuning [2].”
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning (2020, unknown): “This approach can be formalized further by using grammar-based genetic programming (de Sa et al., 2017).”
Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar (2019, background): “…et al., 2017), Bayesian optimization (Rasmussen, 2003; Bergstra and Bengio, 2012; Feurer et al., 2015; Kotthoﬀ et al., 2017), collaborative ﬁltering (Yang et al., 2018; Fusi et al., 2018), and grammars (de S´a et al., 2017), for ﬁnding machine learning pipelines for a given dataset and task.”
Automated Machine Learning: Methods, Systems, Challenges (2019, unknown): “[39] then optimize both the network weights and the network architecture by alternating gradient descent steps on training data for weights and on validation data for architectural parameters such as λ.”