Auto-WEKA

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 25 articles )

Showing results 1 to 20 of 25.
Sorted by year (citations)

1 2 next

  1. Antonio, Candelieri: Sequential model based optimization of partially defined functions under unknown constraints (2021)
  2. Zöller, Marc-André; Huber, Marco F.: Benchmark and survey of automated machine learning frameworks (2021)
  3. 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
  4. Rohan Anand, Joeran Beel: Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization (2020) arXiv
  5. Sandeep Singh Sandha, Mohit Aggarwal, Igor Fedorov, Mani Srivastava: MANGO: A Python Library for Parallel Hyperparameter Tuning (2020) arXiv
  6. van Engelen, Jesper E.; Hoos, Holger H.: A survey on semi-supervised learning (2020)
  7. 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)
  8. Eggensperger, Katharina; Lindauer, Marius; Hutter, Frank: Pitfalls and best practices in algorithm configuration (2019)
  9. Lindauer, Marius; van Rijn, Jan N.; Kotthoff, Lars: The algorithm selection competitions 2015 and 2017 (2019)
  10. Mateusz Staniak, Przemyslaw Biecek: The Landscape of R Packages for Automated Exploratory Data Analysis (2019) arXiv
  11. 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)
  12. Brazdil, Pavel (ed.); Giraud-Carrier, Christophe (ed.): Metalearning and algorithm selection: progress, state of the art and introduction to the 2018 special issue (2018)
  13. Eggensperger, Katharina; Lindauer, Marius; Hoos, Holger H.; Hutter, Frank; Leyton-Brown, Kevin: Efficient benchmarking of algorithm configurators via model-based surrogates (2018)
  14. Haifeng Jin, Qingquan Song, Xia Hu: Auto-Keras: An Efficient Neural Architecture Search System (2018) arXiv
  15. Lorena, Ana C.; Maciel, Aron I.; de Miranda, Péricles B. C.; Costa, Ivan G.; Prudêncio, Ricardo B. C.: Data complexity meta-features for regression problems (2018)
  16. Melnikov, Vitalik; Hüllermeier, Eyke: On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis (2018)
  17. Mohr, Felix; Wever, Marcel; Hüllermeier, Eyke: ML-plan: automated machine learning via hierarchical planning (2018)
  18. Wistuba, Martin; Schilling, Nicolas; Schmidt-Thieme, Lars: Scalable Gaussian process-based transfer surrogates for hyperparameter optimization (2018)
  19. Hutter, Frank; Lindauer, Marius; Balint, Adrian; Bayless, Sam; Hoos, Holger; Leyton-Brown, Kevin: The configurable SAT solver challenge (CSSC) (2017)
  20. Lindauer, Marius; Hoos, Holger; Leyton-Brown, Kevin; Schaub, Torsten: Automatic construction of parallel portfolios via algorithm configuration (2017)

1 2 next