ICEC'09 2009 - PAKDD'09 Workshop on Data Mining When Imbalanced Classes and Errors in Costs (ICEC'09)

PAKDD'2009 Workshop:
Data Mining When Classes are Imbalanced and Errors Have Costs

Tuesday, April 27, 2009

Bangkok, Thailand

Organizers:
Nitesh Chawla University of Notre Dame (nchawla-AT-cse.nd.edu)
Nathalie Japkowicz University of Ottawa (nat-AT-site.uottawa.ca)
Zhi-Hua Zhou Nanjing University (zhouzh-AT-nju.edu.cn)

Publications and Web Chair:

David Cieslak University of Notre Dame (david.cieslak-AT-gmail.com)
Workshop Description:

Overview: Recent years brought increased interest in applying data mining techniques to difficult "real-world" problems, many of which are characterized by imbalanced learning data, where at least one class is under-represented relative to others. Examples include (but are not limited to): fraud/intrusion detection, risk management, medical diagnosis/monitoring, bioinformatics, text categorization and personalization of information. The problem of imbalanced data is also often associated with asymmetric costs of misclassifying elements of different classes. Additionally the distribution of the test data may differ from that of the learning sample and the true misclassification costs may be unknown at learning time.

The AAAI-2000 and ICML 2003 Workshops on "Learning from Imbalanced Data Sets" provided venues where this important problem was explicitly addressed and has been received with much interest. Although much awareness of the issues related to class imbalance has been raised, many of the key problems still remain open and are in fact encountered more often, especially when applied to massive datasets. We believe that it would be of value to the data mining community to not only examine the progress achieved in this area over the last five years but also discuss the current school of thought on research in learning from imbalanced datasets and cost-sensitive learning.

sampling (under-, over-, progressive, active)
accounting for class imbalance via inductive bias
one-sided learning
handling uncertainty of target distribution and misclassification costs
handling varying amounts (class dependent) of label noise
applications confounded by such issues
cost-sensitive learning
imbalance in streaming data
post-processing of learned models

The workshop will offer a strong topic for foundations in data mining with highly compelling interdisciplinary connections. This workshop will appeal to both the algorithm developers and practitioners, as it provides a unique perspective and connection between theory and practitioner. The best papers from the Workshop will be published in a Journal Special Issue.

Proposed Format: The workshop will open with an invited talk (speaker TBD) that will introduce and overview the topic. Presentations will then be organized into several sessions corresponding roughly to the to the categories identified above. The workshop will conclude with a discussion during which a distinguished guest will comment on the presentations of the day, and open the floor for general discussion.

Proposed Length: One Day during which each panel will be allocated 1 to 2 hours, depending on the number of contributions and the expected length of the discussion session.

Workshop Notes: The accepted papers will be available electronically from the workhop website, and also as printed workshop notes to the attendees.

Submissions: Authors are invited to submit papers on the topics outlined above or on other related issues. We also welcome short position papers. Submissions should not exceed 12 pages in the Springer's lecture notes format. We will be following exactly same format as the main conference submission. So, please adhere to the conference style files and instruction, also available here: http://itpe.siit.tu.ac.th/pakdd2009/front/show/cal...

Submitting a paper to the workshop also means that if the paper is accepted, at least one author must attend the workshop to present the paper. The paper may be rescinded, otherwise. The registration fees for the workshop will be determined by the conference, and will be paid directly to the conference. Electronic submissions, in PDF format, should be sent to David Cieslak at david.cieslak-AT-gmail.com.

We will nominate a best methodology paper and best applications paper from the workshop proceedings. We are also considering a journal special issue after the Workshop, and outstanding papers from the workshop will be invited to extend their work and submit for journal publication.

Timetable:

Submission deadline: January 5th, 2009
Notification date: January 25th, 2009
Final date for camera-ready copies to organizers: Feb 5th, 2009
Invited Speakers:

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Program Committee:

Gustavo Batista University of Sao Paulo, Brazil
Sanjay Chawla University of Sydney, Australia
David Cieslak University of Notre Dame, USA
Chris Drummond National Research Council, Canada
Seyda Ertekin Penn State University, USA
George Forman HP Labs, USA
Robert Holte University of Alberta, Canada
W. Philip Kegelmeyer Sandia National Labs, USA
Taghi M. Khoshgoftaar Florida Atlantic University, USA
Alek Kolcz Microsoft Research, USA
Miroslav Kubat University of Miami, USA
Charles Ling University of Waterloo, Canada
Xu-Ying Liu Nanjing University, China
Dragos Margineantu Boeing Phantom Works, USA
Stan Matwin University of Ottawa, Canada
Yuchun Tang Georgia State University
Gary Weiss Fordham University, USA