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Cost-Sensitive Learning for Confidential Access Control
Y. Seo, J. Bagnell, and K. Sycara
tech. report CMU-RI-TR-05-31, Robotics Institute, Carnegie Mellon University, June, 2005.

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It is common to control access to critical information based on the need-to-know principle; The requests for access are authorized only if the content of the requested information is relevant to the requester's project. We formulate such a dichotomous decision in a machine learning framework. Although the cost for misclassifying examples should be differentiated according to their importance, the best-performing error-minimizing classifiers do not have ways of incorporating the cost information into their learning processes. In order to handle the cost effectively, we apply two cost-sensitive learning methods to the problem of the confidential access control and compare their usefulness with those of error-minimizing classifiers. We devise a new metric for assigning cost to any datasets. From the comparison of the cost-sensitive classifiers with error-minimizing classifiers, we find that costing demonstrates the best performance in that it minimizes the cost for misclassifying the examples and the false positive using a relatively small amount of training data.

Sponsor: ARDA CMU subcontract from CTA, DARPA
Grant ID: F30602-03-C-0009

Associated center: CIMDS
Associated lab/group: Intelligent Software Agents

Number of pages: 26

Y. Seo, J. Bagnell, and K. Sycara, Cost-Sensitive Learning for Confidential Access Control, tech. report CMU-RI-TR-05-31, Robotics Institute, Carnegie Mellon University, June, 2005.

@techreport{Seo_2005_5078,
   author = "Young-Woo Seo and James (Drew) Bagnell and Katia Sycara",
   title = "Cost-Sensitive Learning for Confidential Access Control",
   institution = "Robotics Institute, Carnegie Mellon University",
   month = "June",
   year = "2005",
   number = "CMU-RI-TR-05-31",
   address = "Pittsburgh, PA"
}