Science, technology, and commerce increasingly recognise the importance of machine learning approaches for data-intensive, evidence-based decision making. This is accompanied by increasing numbers of machine learning applications and volumes of data. Nevertheless, the capacities of processing systems or human supervisors or domain experts remain limited in real-world applications. Furthermore, many applications require fast reaction to new situations, which means that first predictive models need to be available even if little data is yet available. Therefore approaches are needed that optimise the whole learning process, including the interaction with human supervisors, processing systems, and data of various kind and at different timings: techniques for estimating the impact of additional resources (e.g. data) on the learning progress; techniques for the active selection of the information processed or queried; techniques for reusing knowledge across time, domains, or tasks, by identifying similarities and adaptation to changes between them; techniques for making use of different types of information, such as labeled or unlabeled data, constraints or domain knowledge. Such techniques are studied for example in the fields of adaptive, active, semi-supervised, and transfer learning. However, this is mostly done in separate lines of research, while combinations thereof in interactive and adaptive machine learning systems that are capable of operating under various constraints, and thereby address the immanent real-world challenges of volume, velocity and variability of data and data mining systems, are rarely reported. Therefore, this workshop aims to bring together researchers and practitioners from these different areas, and to stimulate research in interactive and adaptive machine learning systems as a whole. It continues a successful series of events at ECML PKDD 2017 in Skopje (Workshop and Tutorial), IJCNN 2018 in Rio (Tutorial), ECML PKDD 2018 in Dublin (Workshop), ECML PKDD 2019 in Wu¨rzburg (Workshop and Tutorial), and virtual ECML PKDD 2020 (Workshop).
The workshop aims at discussing techniques and approaches for optimising the whole learning process, including the interaction with human supervisors, processing systems, and includes adaptive, active, semi-supervised, and transfer learning techniques, and combinations thereof in interactive and adaptive machine learning systems. Our objective is to bridge the communities researching and developing these techniques and systems in machine learning and data mining. Therefore, we welcome contributions that present a novel problem setting, propose a novel approach, or report experience with the practical deployment of such a system and raise unsolved questions to the research community. All in all, we accepted 10 papers (13 papers submitted) to be published in these workshop proceedings. The authors discuss approaches, identify challenges and gaps between active learning research and meaningful applications, as well as define new application-relevant research directions. We thank the authors for their submissions and the program committee for their hard work.
Andreas Holzinger Daniel Kottke Georg Krempl Vincent Lemaire
Mirko Bunse Klemens B¨ohm Hudelot C´eline Gregory Ditzler Michael Granitzer Marek Herde Martin Holena Denis Huseljic Edwin Lughofer Bernhard Pfahringer Stefano Teso Indre Zliobaite
Dortmund University Karlsruhe Institute of Technology Ecole Centrale Paris University of Arizona University of Passau University of Kassel Institute of Computer Science University of Kassel Johannes Kepler University Linz University of Waikato Katholieke Universiteit Leuven University of Helsinki