Cost-Sensitive Learning for Confidential Access Control

Young-Woo Seo, J. Andrew (Drew) Bagnell and Katia 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.

author = {Young-Woo Seo and J. Andrew (Drew) Bagnell and Katia Sycara},
title = {Cost-Sensitive Learning for Confidential Access Control},
year = {2005},
month = {June},
institution = {Carnegie Mellon University},
address = {Pittsburgh, PA},
number = {CMU-RI-TR-05-31},
keywords = {cost-sensitive learning, confidential access control, machine learning, text learning, artificial intelligence},
} 2017-09-13T10:43:24-04:00