Introduction to the special issue on handling concept drift in adaptive information systems

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Modern information systems collect data from multiple sources, process it, extract information and use it for decision support or decision making. Predictive modeling is an important component of an information system that makes the system intelligent. This special issue focuses on adaptive information systems that can adjust their behavior relying on additional mechanisms continuously monitoring the operational setting and/or the performance of predictive models. Adaptive information systems have become ubiquitous in various application areas including online businesses, personal information access, industry, medicine, education, defence, and in which predictive analytics is an important decision making or decision support component. In the real world data is often non stationary. In predictive analytics, machine learning and data mining the phenomenon of unexpected change in underlying data over time is known as concept drift. Changes in underlying data may occur due to changing personal interests, changes in population, adversary activities or they can be attributed to the complex nature of the environment. When data drifts, predictions may become less accurate as the time passes or opportunities to improve the accuracy may be missed. Thus, the learning models need to be able to adapt automatically to changes over time. The problem of concept drift is of increasing importance in machine learning and data mining since more and more data is organized in the form of data streams rather than static databases, and it is rather unusual that concepts and data distributions stay stable over long periods of time. It is not surprising that the problem of concept drift has been studied in several research communities including but not limited to machine learning and data mining, data streams, information retrieval, and recommender systems. Different approaches for detecting and handling concept drift have been proposed in the literature, and many of them have already proved their potential in a wide range of application domains, e.g. fraud detection, adaptive system control, user modeling, information retrieval, text mining, biomedicine. Moreover, a fast growing scope of applications that rely on data arriving in real time where very often the problem of data drift is observed and recognized to be important, helped to identify and shape a number of new important research challenges that have not been wellstudied in the research communities yet. This special issue includes selected contributions from the first and the second International Workshops on Handling Concept Drift in Adaptive Information Systems; HaCDAIS at ECMLPKDD 2010 and HaCDAIS at IEEE ICDM 2011. The papers address both methodological issues and practical challenges for handling concept drift, such as (a) label availability, (b) recurring concepts, (c) systematic handling of event detection, and (d) mining changes in customer profiling and medical anesthesia domains. We hope you will find the following papers interesting for reading. The first paper ‘‘Drift Detection Using Uncertainty Distribution Divergence’’ by Patrick Lindstrom, Brian Mac Namee and Sarah Jane Delany addresses the problem of M. Pechenizkiy Eindhoven University of Technology, Eindhoven, The Netherlands e-mail: m.pechenizkiy@tue.nl