HaCDAIS 2011: The 2nd International Workshop on Handling Concept Drift in Adaptive Information Systems



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 might occur due to changing personal interests, changes in population, adversary activities or they can be attributed to a complex nature of the environment.

When there is a shift in data, the predictions might become less accurate as the time passes or opportunities to improve the accuracy might be missed. Thus the learning models need to be adaptive to the changes.

The problem of concept drift is of increasing importance to machine learning and data mining as 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 a long period 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.


In this workshop, we aim to attract researchers with an interest in handling concept drift and recurring contexts in adaptive information systems. Although we have emphasized the application aspects of handling concept drift we are open to any original work in this area.

A non-exhaustive list of topics includes:

  • Classification and clustering on data streams and evolving data
  • Change and novelty detection in online, semi-online and offline settings
  • Adaptive ensembles
  • Adaptive sampling and instance selection
  • Incremental learning and model adaptivity
  • Delayed labeling in data streams
  • Dynamic feature selection
  • Handling local and complex concept drift
  • Qualitative and quantitative evaluation of concept drift handling performance
  • Reoccurring contexts and context-aware approaches
  • Application-specific and domain driven approaches within the areas of information retrieval, recommender systems, pattern recognition, user modeling, decision support and adaptive (information) systems

We invite submissions in the following categories:

  • New approaches advancing the current state of the art
  • Generic frameworks for handing concept drift and reoccurring contexts
  • Taxonomies and categorizations of the approaches for handing concept drift and reoccurring contexts
  • Case studies and application examples dealing with drifting data

Please notice that we encourage prospective contributors to submit full papers (10 pages) as short papers (5 pages).


July 23, 2011 Submission due (for both full and short papers)

September 20, 2011 Notification of acceptance

October 11, 2011 Final papers due

December 10, 2011 Workshop day


Paper submissions are limited to a maximum of 10 pages in the IEEE 2-column format, which is the same as the camera-ready format (see the IEEE Computer Society Press Proceedings Author Guidelines). All papers will be reviewed by the Program Committee based on technical quality, relevance to data mining, originality, significance, and clarity. A double blind reviewing process will be adopted. Authors should therefore avoid using identifying information in the text of the paper. All papers should be submitted through the ICDM Workshop Submission Site. At the time of submission, the papers must not be under review or accepted for publication elsewhere except the main IEEE ICDM conference.

All accepted workshop papers will be published in a separate ICDM workshop proceedings published by the IEEE Computer Society Press. In addition, authors with accepted papers to the workshop will have the opportunity to be invited to publish their extended versions to a special issues in a journal.


HaCDAIS 2011 is the 2nd workshop focusing on handling concept drift and reoccuring contexts in adaptive information systems. The 1st HaCDAIS workshop was held in conjunction with ECML/PKDD 2010 in Barcelona, Catalonia, Spain. Several other events are addressing the problem of changing data and this way are related to HaCDAIS: International Workshop on Knowledge Discovery from Sensor Data (SensorKDD), Novel Data Stream Pattern Mining Techniques (StreamKDD), Data Streams Track at ACM Symposium on Applied Computing (SAC10), Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE 2011), Concept Drift and Learning in Nonstationary Environments at IEEE World Congress on Computational Intelligence .


Latifur Khan University of Texas, Dallas, USA

Mykola Pechenizkiy Eindhoven University of Technology, the Netherlands

Indrė Žliobaitė Bournemouth University, UK