  To solve a machine learning problem, one typically needs to perform data
preprocessing, modeling, and hyperparameter tuning, which is known as model
selection and hyperparameter optimization.The goal of automated machine
learning (AutoML) is to design methods that can automatically perform model
selection and hyperparameter optimization without human interventions for a
given dataset. In this paper, we propose a meta-learning method that can search
for a high-performance machine learning pipeline from the predefined set of
candidate pipelines for supervised classification datasets in an efficient way
by leveraging meta-data collected from previous experiments. More specifically,
our method combines an adaptive Bayesian regression model with a neural network
basis function and the acquisition function from Bayesian optimization. The
adaptive Bayesian regression model is able to capture knowledge from previous
meta-data and thus make predictions of the performances of machine learning
pipelines on a new dataset. The acquisition function is then used to guide the
search of possible pipelines based on the predictions.The experiments
demonstrate that our approach can quickly identify high-performance pipelines
for a range of test datasets and outperforms the baseline methods.
