Getting Ready for Change: Handling Concept Drift in Predictive Analytics

In the real world data often arrives in streams and evolves over time. Concept drift in supervised learning means that the relation between the input data and the target variable changes. Therefore, in many real-world applications the learning models need to adapt to the anticipated changes. In this talk I will overview the state of the art in concept drift research in data mining and related areas. First, I will introduce the problem of concept drift with illustrative real-world examples, characterize adaptive learning process, categorize existing strategies for (reactive) handling concept drift in the most assumed setting – unpredictable changes happen in hidden contexts that are not observable to the adaptive learning system. Then, I will show why from the application perspective it is interesting to look into several other operational settings that commonly occur in practice, but have been underexplored in academia. In particular, I will show that there is a room for proactive approaches for handling. I will conclude the talk with an overview of the recent trends and next challenges in concept drift research.

Speaker Details

Mykola Pechenizkiy is Assistant Professor in the Web Engineering group at the Department of Computer Science, Eindhoven University of Technology, the Netherlands. He received his PhD from the University of Jyvaskyla, Finland in 2005. He has broad expertise and research interests in data science, including also applications to medicine, industry and education. He develops generic frameworks and effective approaches for designing adaptive, context-aware predictive analytics systems that can deal with evolving data and are applicable to a wide range of (Web) information systems.

He has co-authored over 70 peer-reviewed publications and co-organized several workshops (HaCDAIS@ECMLPKDD’10 &@ICDM’11, LEMEDS@AIME’11), conferences (BNAIC’09, EDM’11, CBMS’12) and tutorials (@CBMS’10, ECMLPKDD’10, PAKDD’11, ECMLPKDD’12) in these areas. He has co-edited the Handbook of Educational Data Mining and served as a guest editor of the special issues with SIGKDD Explorations, Evolving Systems, Data and Knowledge Engineering, and Artificial Intelligence in Medicine journals. He has actively collaborated with industry through internationally-, nationally-, and industry-funded projects (Philips Research, VTT, Sanoma Media Group, Sligro Food Group, StudyPortals, Teezir, Adversitement, ASML, C-Content, Multiscope). Since 2002, Mykola has been a research visitor to several universities, including Aalto University, U. Bournemouth, U. Cordoba, U. Jyvaskyla, Nanyang Technological University, U. Porto, U. Portsmouth, U. Sydney, Trinity College Dublin, and U. Ulster. Since June 2013 he is also Adjunct Professor (Dosentti) at the Department of the Mathematical Information Technology, University of Jyväskylä.