Invitation to Participate, Call for Contributions

Workshop on COST-SENSITIVE LEARNING

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


Workshop Motivation and Description

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. However, existing classification algorithms assume that the input features are already measured (at no cost) and that the goal is to minimize the number of misclassification errors (the 0/1-loss).

For example, in medical diagnosis, different tests have different costs (and risks) and different outcomes (false positives and false negatives) have different costs. The cost of a false positive medical diagnosis is an unnecessary treatment, but the cost of a false negative diagnosis may be the death of the patient. Given a choice, a cost-sensitive learning algorithm should prefer to measure less costly features and to make less costly errors (in this example, false positives). Not surprisingly, when existing learning algorithms are applied to cost-sensitive problems, the results are often poor, because they have no way of making these tradeoffs.

Another example concerns the timeliness of predictions in time-series applications. Consider a classifier that is applied to monitor a complex system (e.g., factor, power plant, medical device). It is supposed to signal an alarm if a problem is about to occur. The value of the alarm is not merely related to whether it is a false alarm or a missed alarm, but also to whether the alarm is raised soon enough to allow preventative measures to be taken.

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. A good reference on different types of costs, and cost-sensitive learning can be found here.
The workshop will be structured around three main topics:

  • Algorithms for Cost-Sensitive Learning
    - Algorithms that take cost information as input (along with the training data) and produce a cost-sensitive classifier as output.
    - Algorithms that construct robust classifiers that accept cost information at classification time.
    - Algorithms designed for other types of costs.
  • Costs and Loss Functions that Arise in Real-World Applications
    - What types of costs are involved in practical applications?
    - What are the loss functions in current and future applications of machine learning?
    - What are the right ways of formulating various cost-sensitive learning problems?
  • Methods and Promising Directions for Future Research
    - What methods should be applied to evaluate cost-sensitive learning algorithms?
    - What are promising new directions to pursue?
    - What should be our ultimate research goals?

  • Approximately one third of the day will be devoted to each of these three topics. On each of these topics, one or two of the leading researchers will give presentations. These will be followed by a mix of discussion and short position papers presented by the participants.


    Submissions
      To participate in the workshop, please send a email message to Tom Dietterich (tgd@cs.orst.edu) giving your name, address, email address, and a brief description of your reasons for wanting to attend. In addition, if you wish to present one or more position papers on the topics listed above, please send a one-page abstract of each position paper to Tom Dietterich at the same email address. You may submit a position paper on each of the three main topics (algorithms, loss functions, future research). If you have an issue or contribution that is not covered by these three categories, please contact Tom Dietterich by email to discuss your idea prior to submitting a position paper. Submissions are especially solicited that describe the loss functions arising in real-world applications of machine learning. The organizers will review the submissions with the goal of assembling a stimulating and exciting workshop. Attendence will be limited to 40 people, with preference given to people who are presenting position papers.


    Important dates
    • Submission deadline: April 24, 2000
    • Notification of acceptance: May 8, 2000
    • Workshop to be held on July 2, 2000


    Organizers