IEEE HaCDAIS 2011: The 2nd International Workshop on Handling Concept Drift in Adaptive Information Systems will be organized at IEEE ICDM 2011 in Vancouver, Canada, December 10th, 2011|
Tentative schedule of the workshop and proceedings are available here
International Workshop on Handling Concept Drift in Adaptive Information Systems: Importance, Challenges and Solutions will take place in Casa Convalescencia, Barcelona, Catalonia, Spain on September 24th, 2010. It is organized in conjunction with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2010).
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.
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 "Handling Concept Drift in Data Streams: From Theory to Applications" by João Gama and MOA: Massive Online Analysis framework and software demonstration by Albert Bifet.
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.
Please notice that prior to the workshop there will be a Tutorial on Learning from Evolving Data which will also include an introduction to the state of the art in concept drift research.
João Gama is Associate Professor at Faculty of Economics, University of Porto. He is a researcher at LIAAD-INESC Porto LA. His main research interest is Learning from Data Streams. He has published more than 70 articles in international journals and conferences, and advised 2 successful PhD dissertations. He served as Co-chair of ECML 2005, DS 2009 and ADMA 2009. He is the author of a recent book on Knowledge Discovery from Data Streams.
MOA (Massive Online Analysis) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed to deal with the challenging problem of scaling up the implementation of state of the art algorithms to real world dataset sizes. MOA includes classification and clustering methods. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and is released under the GNU GPL license.
Albert Bifet is a Postdoctoral Research Fellow at the Machine Learning Group at the University of Waikato in Hamilton, New Zealand. He obtained a Ph.D. from UPC-Barcelona Tech. He is the author of a book on Adaptive Stream Mining and Pattern Learning and Mining from Evolving Data Streams.
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.
We invite submissions in the following categories:
Full papers (up to 12 pages in Springer LNCS format) and extended abstracts (up to 4 pages in Springer LNCS format) are invited. Authors of the accepted extended abstracts will be asked to submit a short paper (4 to 8 pages Springer LNCS format) that describes their research in more detail. Papers in PDF should be submitted to firstname.lastname@example.org.
Each submitted paper will be reviewed by at least two reviewers. Submission implies the willingness of at least one of the authors to register and present the paper.
Final versions of the accepted papers will be published in the informal ECML/PKDD workshop proceedings and will be made available on the workshop website before the workshop takes place.
Online proceedings as a single pdf file.
HaCDAIS 2010 is the first workshop focusing on handling concept drift and reoccuring contexts in adaptive information systems. Several other events are addressing the problem of changing data and this way are related to HaCDAIS 2010: 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 .