HaCDAIS 2011 - International Workshop on Handling Concept Drift and Reoccurring Contexts in Adaptive Information Systems.

The 2nd International Workshop on Handling Concept Drift in Adaptive Information Systems will take place in Vancouver, Canada on December 10th, 2011. It is organized in conjunction with the 11th IEEE International Conference on Data Mining (IEEE ICDM 2011).

The objective of the workshop is to provide a forum for discussion of recent advances in handling concept drift in adaptive information systems, and to offer an opportunity for researchers and practitioners to identify and discuss recent advances and new promising research directions.

WORKSHOP FORMAT

The workshop will take half a day. Besides regular sessions consisting of presentations of selected peer-reviewed papers, the programme will feature an invited talk from industry

In the closing session, we will lead an open discussion aimed to foresee the future of concept drift research and to identify immediate opportunities for collaboration.
CALL FOR PAPERS (in pdf, in txt)

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.

TOPICS OF INTEREST

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).