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 22 articles )

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  1. Sandeep Singh Sandha, Mohit Aggarwal, Igor Fedorov, Mani Srivastava: MANGO: A Python Library for Parallel Hyperparameter Tuning (2020) arXiv
  2. van Engelen, Jesper E.; Hoos, Holger H.: A survey on semi-supervised learning (2020)
  3. 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)
  4. Lindauer, Marius; van Rijn, Jan N.; Kotthoff, Lars: The algorithm selection competitions 2015 and 2017 (2019)
  5. Mateusz Staniak, Przemyslaw Biecek: The Landscape of R Packages for Automated Exploratory Data Analysis (2019) arXiv
  6. 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)
  7. Brazdil, Pavel (ed.); Giraud-Carrier, Christophe (ed.): Metalearning and algorithm selection: progress, state of the art and introduction to the 2018 special issue (2018)
  8. Eggensperger, Katharina; Lindauer, Marius; Hoos, Holger H.; Hutter, Frank; Leyton-Brown, Kevin: Efficient benchmarking of algorithm configurators via model-based surrogates (2018)
  9. Kordík, Pavel; Černý, Jan; Frýda, Tomáš: Discovering predictive ensembles for transfer learning and meta-learning (2018)
  10. 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)
  11. Melnikov, Vitalik; Hüllermeier, Eyke: On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis (2018)
  12. Mohr, Felix; Wever, Marcel; Hüllermeier, Eyke: ML-plan: automated machine learning via hierarchical planning (2018)
  13. Olier, Ivan; Sadawi, Noureddin; Bickerton, G. Richard; Vanschoren, Joaquin; Grosan, Crina; Soldatova, Larisa; King, Ross D.: Meta-QSAR: a large-scale application of meta-learning to drug design and discovery (2018)
  14. Wistuba, Martin; Schilling, Nicolas; Schmidt-Thieme, Lars: Scalable Gaussian process-based transfer surrogates for hyperparameter optimization (2018)
  15. Hutter, Frank; Lindauer, Marius; Balint, Adrian; Bayless, Sam; Hoos, Holger; Leyton-Brown, Kevin: The configurable SAT solver challenge (CSSC) (2017)
  16. Lindauer, Marius; Hoos, Holger; Leyton-Brown, Kevin; Schaub, Torsten: Automatic construction of parallel portfolios via algorithm configuration (2017)
  17. Mısır, Mustafa; Sebag, Michèle: \textscAlors: an algorithm recommender system (2017)
  18. Van Craenendonck, Toon; Blockeel, Hendrik: Constraint-based clustering selection (2017)
  19. Yu-Ren Liu, Yi-Qi Hu, Hong Qian, Yang Yu, Chao Qian: ZOOpt/ZOOjl: Toolbox for Derivative-Free Optimization (2017) arXiv
  20. Bischl, Bernd; Kerschke, Pascal; Kotthoff, Lars; Lindauer, Marius; Malitsky, Yuri; Fréchette, Alexandre; Hoos, Holger; Hutter, Frank; Leyton-Brown, Kevin; Tierney, Kevin; Vanschoren, Joaquin: ASlib: a benchmark library for algorithm selection (2016)

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