HaCDAIS 2010:
International Workshop on Handling Concept Drift
in Adaptive Information Systems:
Importance, Challenges and Solutions
Information

Program   

Attending   
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

WELCOME

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.

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 "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.

INVITED SPEAKER

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.

SOFTWARE DEMO

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.

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
  • Data generators, benchmark datasets, evaluation frameworks, and software reports
  • Discussion/position papers of the more provocative nature, open problems and challenges

Please notice that we encourage prospective contributors to submit as full papers as extended abstracts.

IMPORTANT DATES

June 28, 2010 (extended) Submission due (for both full papers and extended abstracts)
July 12, 2010 Notification of acceptance
July 21, 2010 Final papers due
September 24, 2010 Workshop day

SUBMISSION PROCEDURE

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 hacdais2010@gmail.com.

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

Online proceedings as a single pdf file.

Individual papers can be also downloaded by clicking the corresponding paper title in the schedule section below.

SCHEDULE (tentative)

Session 1: 15:00 - 16:30
14.55 - 15.00 Introduction to the workshop from the organizers
15.00 - 15.25 Invited Talk: "Reasoning about the Learning Process"
João Gama
15.25 - 16.45 Handling concept drift in preference learning for interactive decision making
Paolo Campigotto, Andrea Passerini, and Roberto Battiti
15.45 - 16.05 Incremental option trees for handling gradual concept drift
Elena Ikonomovska, João Gama, and Sašo Džeroski
16.05 - 16.30 Invited Software Demo:
"MOA: Massive Online Analysis, a framework for stream classification and clustering"
Albert Bifet
Coffee break: 16:30 - 17:00
Session 2: 17:00 - 18:30
17.00 - 17.15 Cost-sensitive boosting for concept drift
Ashok Venkatesan, Narayanan C. Krishnan, Sethuraman Panchanathan
17.15 - 17.30 An adaptive hybrid recommender system that learns domain dynamics
Fatih Aksel and Aysenur Birtü rk
17.30 - 17.45 Modeling the example life-cycle in an online classification learner
Gary R. Marrs, Ray J. Hickey, and Michaela M. Black
17.45 - 18.00 An incremental text segmentation by clustering cohesion
Raúl Abella Pérez and José Eladio Medina Pagola
18.00 - 18.30Closing discussion

RELATED EVENTS

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 .

WORKSHOP CHAIRS

Mykola PechenizkiyEindhoven University of Technology, the Netherlands
Indrė Žliobaitė Eindhoven University of Technology, the Netherlands

PROGRAMME COMMITTEE

Albert Bifet University of Waikato, New Zealand
Sarah Jane Delany Digital Media Centre, Ireland
Anton Dries Katholieke Universiteit Leuven, Belgium
Bogdan Gabrys Bournemouth University, UK
João Gama University of Porto, Portugal
Ioannis Katakis Aristotle University of Thessaloniki, Greece
Yehuda Koren Yahoo! Research, Israel
Ludmila Kuncheva Bangor University, UK
Matthijs van Leeuwen Universiteit Utrecht, the Netherlands
Ernestina Menasalvas Universidad Politecnica de Madrid, Spain
Robi Polikar Rowan University, USA
Myra Spiliopoulou Otto-von-Guericke-University Magdeburg, Germany
Alexey Tsymbal Siemens AG, Germany
Athena Vakali Aristotle University, Thessaloniki, Greece

For further questions, please contact organizers at hacdais2010@gmail.com