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