@incollection{kotthoff_auto-weka_2019,
  address = {Cham},
  title = {Auto-{WEKA}: {Automatic} {Model} {Selection} and {Hyperparameter} {Optimization} in {WEKA}},
  isbn = {978-3-030-05318-5},
  url = {https://doi.org/10.1007/978-3-030-05318-5_4},
  abstract = {Many different machine learning algorithms exist; taking into account each algorithm's hyperparameters, there is a staggeringly large number of possible alternatives overall. We consider the problem of simultaneously selecting a learning algorithm and setting its hyperparameters. We show that this problem can be addressed by a fully automated approach, leveraging recent innovations in Bayesian optimization. Specifically, we consider feature selection techniques and all machine learning approaches implemented in WEKA's standard distribution, spanning 2 ensemble methods, 10 meta-methods, 28 base learners, and hyperparameter settings for each learner. On each of 21 popular datasets from the UCI repository, the KDD Cup 09, variants of the MNIST dataset and CIFAR-10, we show performance often much better than using standard selection and hyperparameter optimization methods. We hope that our approach will help non-expert users to more effectively identify machine learning algorithms and hyperparameter settings appropriate to their applications, and hence to achieve improved performance.},
  booktitle = {Automated {Machine} {Learning}: {Methods}, {Systems}, {Challenges}},
  publisher = {Springer International Publishing},
  author = {Kotthoff, Lars and Thornton, Chris and Hoos, Holger H. and Hutter, Frank and Leyton-Brown, Kevin},
  editor = {Hutter, Frank and Kotthoff, Lars and Vanschoren, Joaquin},
  year = {2019},
  doi = {10.1007/978-3-030-05318-5_4},
  pages = {81--95}
}