PAKDD 2011 Tutorial: Handling Concept Drift: Importance, Challenges and Solutions

Tutorial at PAKDD discussing concept drift, and MOA as an open source software to deal with concept drift.

Abstract: In the real world data often arrives in streams and is evolving over time. Concept drift in supervised learning means that the underlying distribution of the data is changing. As a result the predictions might become less accurate as the time passes, or opportunities to improve the accuracy might be missed. Therefore, the learning models need to adapt to changes quickly and accurately. The proposed tutorial aims to provide a unifying view on the basic and applied concept drift research in data mining and related areas. In the first part we will introduce the problem of concept drift, discuss why changes appear in supervised learning and motivation to handle them. We will overview what types of application tasks are available. In the second part we will present available approaches and techniques to handle concept drift, discuss evaluation issues and open source software. In the third part we will reflect on the past, present and future of concept drift research and outline future research directions. We will focus on the link between research scenarios and application needs.


  • Albert Bifet, University of Waikato, New Zealand
  • João Gama, University of Porto, Portugal
  • Mykola Pechenizkiy, Eindhoven University of Technology, Netherlands
  • Indrė Žliobaitė, Eindhoven University of Technology, the Netherlands

Tutorial website