Special Issue on Metalearning and Algorithm Selection

ISSN: 0885-6125 (Print) 1573-0565 (Online)

In this topical collection (10 articles)

  1. Editorial

    Metalearning and Algorithm Selection: progress, state of the art and introduction to the 2018 Special Issue

    Pavel Brazdil, Christophe Giraud-Carrier Pages 1-14
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  2. OriginalPaper

    Efficient benchmarking of algorithm configurators via model-based surrogates

    Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter… Pages 15-41
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  3. OriginalPaper

    Scalable Gaussian process-based transfer surrogates for hyperparameter optimization

    Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme Pages 43-78
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  4. OriginalPaper

    Speeding up algorithm selection using average ranking and active testing by introducing runtime

    Salisu Mamman Abdulrahman, Pavel Brazdil, Jan N. van Rijn… Pages 79-108
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  5. OriginalPaper

    Instance spaces for machine learning classification

    Mario A. Muñoz, Laura Villanova, Davaatseren Baatar, Kate Smith-Miles Pages 109-147
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  6. OriginalPaper

    The online performance estimation framework: heterogeneous ensemble learning for data streams

    Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer… Pages 149-176
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  7. OriginalPaper

    Discovering predictive ensembles for transfer learning and meta-learning

    Pavel Kordík, Jan Černý, Tomáš Frýda Pages 177-207
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  8. OriginalPaper

    Data complexity meta-features for regression problems

    Ana C. Lorena, Aron I. Maciel, Péricles B. C. de Miranda, Ivan G. Costa… Pages 209-246
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  9. OriginalPaper

    Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction

    Brandon Malone, Kustaa Kangas, Matti Järvisalo, Mikko Koivisto… Pages 247-283
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  10. OriginalPaper

    Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

    Ivan Olier, Noureddin Sadawi, G. Richard Bickerton, Joaquin Vanschoren… Pages 285-311
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