UBC Theses and Dissertations
Auto-WEKA : combined selection and hyperparameter optimization of supervised machine learning algorithms Thornton, Chris
Many different machine learning algorithms exist; taking into account each algorithm's set of hyperparameters, there is a staggeringly large number of possible choices. This project considers the problem of simultaneously selecting a learning algorithm and setting its hyperparameters. Previous works attack these issues separately, but this problem can be addressed by a fully automated approach, in particular by leveraging recent innovations in Bayesian optimization. The WEKA software package provides an implementation for a number of feature selection and supervised machine learning algorithms, which we use inside our automated tool, Auto-WEKA. Specifically, we examined the 3 search and 8 evaluator methods for feature selection, as well as all of the classification and regression methods, spanning 2 ensemble methods, 10 meta-methods, 27 base algorithms, and their associated hyperparameters. On 34 popular datasets from the UCI repository, the Delve repository, the KDD Cup 09, variants of the MNIST dataset and CIFAR-10, our method produces classification and regression performance often much better than obtained using state-of-the-art algorithm selection and hyperparameter optimization methods from the literature. Using this integrated approach, users can more effectively identify not only the best machine learning algorithm, but also the corresponding hyperparameter settings and feature selection methods appropriate for that algorithm, and hence achieve improved performance for their specific classification or regression task.
Item Citations and Data
Attribution-NonCommercial-NoDerivs 2.5 Canada