The papers contained in this special issue were accepted to the Machine Learning Journal via the ECML PKDD 2019 journal track. The journal track, which began in 2013, allows authors to have a journal publication and still present their work at a machine learning conference; survey papers or extensions of previously published conference papers were not considered. Authors may submit papers to either the Machine Learning Journal or the Data Mining and Knowledge Discovery Journal. The ECML PKDD 2019 journal track was organized around five submission deadlines between August 2018 and January 2019. Accepted papers were presented at the ECML PKDD 2019 conference in Würzburg, Germany from September 16–20, 2019.

A total of 82 papers were submitted to the Machine Learning Journal, of which 18 were accepted in time for this special issue. The special issue also contains three papers, edited by Jesse Davis, Elisa Fromont, Derek Greene, and Björn Bringman, which were submitted to the journal track for ECML PKDD 2018 but only finished the review process after the special issue was published. Accepted papers cover a multitude of topics within machine learning, such as decision tree learning, reinforcement learning, deep learning, feature selection, mixtures of experts, time series forecasting, robust estimation, and active learning.

We thank all of the authors who submitted papers to the journal track, as well as the members of our Guest Editorial Board and other reviewers who provided timely and high-quality reviews. We especially thank Katharina Heinrich (ETH Zürich), who coordinated the reviewing and editing efforts. We will honor outstanding reviewing service to this special issue through Reviewer Awards, whose winners will be announced on the ECML PKDD 2019 website. We are also grateful for the support of Peter Flach (Editor-in-Chief of the Machine Learning Journal), Johannes Fürnkranz (Editor-in-Chief of the Data Mining and Knowledge Discovery Journal), and Melissa Fearon (Senior Editor of Springer responsible for these journals). Finally, we thank ECML PKDD 2019 general chairs and the ECML PKDD 2018 journal track chairs for their guidance throughout the year. We hope that the readers enjoy the papers in this issue!