CFP: AAAI Workshop on Learning from Imbalanced Data Sets

[Nathalie Japkowicz]

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		       Call for Participation

		      	AAAI-2000 Workshop on
	  	Learning from Imbalanced Data Sets

	            July 31 2000, Austin Texas
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The majority of learning systems previously designed and tested on toy 
problems or carefully crafted benchmark data sets usually assumes that the 
training sets are well balanced. In the case of concept-learning, for 
example, classifiers typically expect that their training set contains as 
many examples of the positive as of the negative class. Unfortunately, this 
balanced assumption is often violated in real world settings. Indeed, there 
exist many domains for which some classes are represented by a large number 
of examples while the others are represented by only a few.

Although the imbalanced data set problem is starting to attract researchers'
attention, attempts at tackling it have remained isolated. It is our
belief that much progress could be achieved from a concerted effort and
a greater amount of interactions between researchers interested in this
issue. The purpose of this workshop is to provide a forum to foster such
interactions and identify future research directions. 


Topics
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* Novel techniques for dealing with imbalanced data sets:

        * Techniques for over-sampling the minority class.

        * Techniques for down-sizing the majority class.

        * Techniques for learning from a single class.

        * Techniques for internally biasing the learning process.

        * Other approaches. 

* Comparing the various methodologies.

* The data imbalance problem in unsupervised learning.


Format
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The workshop will consist of several sessions concentrating on the themes
identified above. The workshop will conclude with a panel of distinguished 
guests commenting on the presentations of the day, discussing future
directions, and opening the floor for general discussion.


Attendance
----------

This workshop is open to all members of the Machine-Learning, Data-Mining,
Information Retrieval, Statistics and Connectionist communities interested 
in the data imbalance problem. Attendance is limited to 65 participants.


Submission
----------

Prospective participants are invited to submit papers on the topics outlined 
above or on other related issues. Submissions should be 6 pages, and be in 
line with the AAAI style sheet. Electronic submissions, in Postscript format, 
are prefered and should be sent to Nathalie Japkowicz at nat at cs.dal.ca. 
Alternatively, four hard copies of the papers can be sent to:

                       Nathalie Japkowicz
                       Faculty of Computer Science
                       DalTech/Dalhousie University
                       6050 University Avenue
                       Halifax, N.S.
                       Canada, B3H 1W5

                       Telephone: (902) 494-3157
                       FAX:       (902) 492-1517

If space is available, attendance to the workshop is also possible by 
submitting a 1 or 2 page statement of interest to the above address.


Timetable: 
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* Submission deadline: March 10, 2000 
* Notification date: March 24, 2000 
* Final date for camera-ready copies to organizers: April 26, 2000 


Co-Chairs:
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* Robert Holte, University of Ottawa (holte at site.uottawa.ca); 
* Nathalie Japkowicz, Dalhousie University (nat at cs.dal.ca); 
* Charles Ling, University of Western Ontario (ling at csd.uwo.ca); 
* Stan Matwin University of Ottawa (stan at site.uottawa.ca)


Additional Information
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http://borg.cs.dal.ca/~nat/Workshop2000/workshop2000.html