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- W1538961503 abstract "Abstract : 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." @default.
- W1538961503 created "2016-06-24" @default.
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- W1538961503 date "2005-06-01" @default.
- W1538961503 modified "2023-09-24" @default.
- W1538961503 title "Cost-Sensitive Learning for Confidential Access Control" @default.
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- W1538961503 doi "https://doi.org/10.21236/ada597125" @default.
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