Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms


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, going beyond previous work that attacks these issues separately. We show that this problem can be addressed by a fully automated approach, leveraging recent innovations in Bayesian optimization. Specifically, we consider a wide range of feature selection techniques (combining 3 search and 8 evaluator methods) and all classification approaches implemented in WEKA's standard distribution, spanning 2 ensemble methods, 10 meta-methods, 27 base classifiers, and hyperparameter settings for each classifier. On each of 21 popular datasets from the UCI repository, the KDD Cup 09, variants of the MNIST dataset and CIFAR-10, we show classification 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.

DOI: 10.1145/2487575.2487629
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@inproceedings{Thornton2013AutoWEKACS, title={Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms}, author={Chris Thornton and Frank Hutter and Holger H. Hoos and Kevin Leyton-Brown}, booktitle={KDD}, year={2013} }