Cost-sensitive learning with conditional Markov networks |
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Authors: | Prithviraj Sen Lise Getoor |
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Affiliation: | (1) Department of Computer Science, University of Maryland, College Park, MD 20783, USA |
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Abstract: | There has been a recent, growing interest in classification and link prediction in structured domains. Methods such as conditional
random fields and relational Markov networks support flexible mechanisms for modeling correlations due to the link structure.
In addition, in many structured domains, there is an interesting structure in the risk or cost function associated with different
misclassifications. There is a rich tradition of cost-sensitive learning applied to unstructured (IID) data. Here we propose
a general framework which can capture correlations in the link structure and handle structured cost functions. We present two new cost-sensitive structured classifiers based on maximum entropy principles. The first determines
the cost-sensitive classification by minimizing the expected cost of misclassification. The second directly determines the
cost-sensitive classification without going through a probability estimation step. We contrast these approaches with an approach
which employs a standard 0/1-loss structured classifier to estimate class conditional probabilities followed by minimization
of the expected cost of misclassification and with a cost-sensitive IID classifier that does not utilize the correlations
present in the link structure. We demonstrate the utility of our cost-sensitive structured classifiers with experiments on
both synthetic and real-world data. |
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Keywords: | Cost-sensitive learning Machine learning Markov networks Structured output spaces |
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