Explorations Special Issue


CALL FOR PAPERS

SIGKDD Explorations Special Issue on Learning from Imbalanced Datasets



Guest editors:



SIGKDD Explorations is the official newsletter of ACM SIGKDD (Special Interest Group on Knowledge Discovery and Data Mining).

This special issue invites submissions concerned with learning from imbalanced data.

Many real-world problems are characterized by imbalanced learning data, where at least one class is under-represented relative to others, and/or where the data available for some of the classes does not reflect the true underlying distribution. In addition, there can be non-uniform costs associated with different types of errors or examples in the data.

Important practical applications include: We invite contributions addressing the issues related to solving data mining involving imbalanced datasets.

Relevant topics include (but are not limited to):

Submissions should be made to chawla@morden.csee.usf.edu, preferably in a PDF format and should not exceed 8-10 pages. In addition, please email the abstract in text-format.

Detailed formatting instructions are available from http://www.acm.org/sigkdd/explorations/instructions.htm.

Submissions will be reviewed externally.

Important Dates:

Submissions ............................................. January 30, 2004
Reviews due back to authors ................... March 5, 2004
Camera-ready due ................................... April 5, 2004