Auto-WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA. WEKA is a widely used, open-source machine learning platform. Due to its intuitive interface, it is particularly popular with novice users. However, such users often find it hard to identify the best approach for their particular dataset among the many available. We describe the new version of Auto-WEKA, a system designed to help such users by automatically searching through the joint space of WEKA’s learning algorithms and their respective hyperparameter settings to maximize performance, using a state-of-the-art Bayesian optimization method. Our new package is tightly integrated with WEKA, making it just as accessible to end users as any other learning algorithm.

References in zbMATH (referenced in 35 articles )

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  1. Antonio, Candelieri: Sequential model based optimization of partially defined functions under unknown constraints (2021)
  2. Bakirov, Rashid; Fay, Damien; Gabrys, Bogdan: Automated adaptation strategies for stream learning (2021)
  3. Corazza, Marco; di Tollo, Giacomo; Fasano, Giovanni; Pesenti, Raffaele: A novel hybrid PSO-based metaheuristic for costly portfolio selection problems (2021)
  4. Qian, Zhaozhi; Alaa, Ahmed M.; van der Schaar, Mihaela: CPAS: the UK’s national machine learning-based hospital capacity planning system for COVID-19 (2021)
  5. Škrlj, Blaž; Martinc, Matej; Lavrač, Nada; Pollak, Senja: autoBOT: evolving neuro-symbolic representations for explainable low resource text classification (2021)
  6. Yang, Zebin; Zhang, Aijun: Hyperparameter optimization via sequential uniform designs (2021)
  7. Zöller, Marc-André; Huber, Marco F.: Benchmark and survey of automated machine learning frameworks (2021)
  8. Calimeri, Francesco; Dodaro, Carmine; Fuscà, Davide; Perri, Simona; Zangari, Jessica: Technical note. Efficiently coupling the (\mathscrI)-DLV grounder with ASP solvers (2020)
  9. D. van Kuppevelt, C. Meijer, F. Huber, A. van der Ploeg, S. Georgievska, V. T. van Hees: Mcfly: Automated deep learning on time series (2020) not zbMATH
  10. Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, Alexander Smola: AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data (2020) arXiv
  11. Pimenta, Cristiano G.; de Sá, Alex G. C.; Ochoa, Gabriela; Pappa, Gisele L.: Fitness landscape analysis of automated machine learning search spaces (2020)
  12. Rohan Anand, Joeran Beel: Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization (2020) arXiv
  13. Sandeep Singh Sandha, Mohit Aggarwal, Igor Fedorov, Mani Srivastava: MANGO: A Python Library for Parallel Hyperparameter Tuning (2020) arXiv
  14. van Engelen, Jesper E.; Hoos, Holger H.: A survey on semi-supervised learning (2020)
  15. Xavier-Júnior, João C.; Freitas, Alex A.; Ludermir, Teresa B.; Feitosa-Neto, Antonino; Barreto, Cephas A. S.: An evolutionary algorithm for automated machine learning focusing on classifier ensembles: an improved algorithm and extended results (2020)
  16. Andonie, Răzvan: Hyperparameter optimization in learning systems (2019)
  17. Eggensperger, Katharina; Lindauer, Marius; Hutter, Frank: Pitfalls and best practices in algorithm configuration (2019)
  18. Lindauer, Marius; van Rijn, Jan N.; Kotthoff, Lars: The algorithm selection competitions 2015 and 2017 (2019)
  19. Mateusz Staniak, Przemyslaw Biecek: The Landscape of R Packages for Automated Exploratory Data Analysis (2019) arXiv
  20. Sánchez-DelaCruz, Eddy; Weber, Roberto; Biswal, R. R.; Mejía, Jose; Hernández-Chan, Gandhi; Gómez-Pozos, Heberto: Gait biomarkers classification by combining assembled algorithms and deep learning: results of a local study (2019)

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