In conjunction with the Seventeenth International Conference on Machine Learning, ICML-2000,
Stanford University, June 29 - July 2, 2000

Recent years have seen supervised learning methods applied to a variety of challenging problems in industry, medicine, and science. In many of these problems, there are costs associated with measuring input features and there are costs associated with different possible outcomes. Most of the existing classification algorithms do not take into consideration these costs.

The goal of this workshop is to bring together researchers who are working on problems for which the standard 0/1-loss model with zero-cost input features is unsatisfactory.

  • Online Workshop Notes

  • Workshop Schedule

  • Call for contributions/Invitation to participate

  • Submission format

  • Important dates

  • Organizers

  • Related Event: AAAI-2000 Workshop on Imbalanced Data

  • Useful Resources: