MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
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ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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ICML '04 Proceedings of the twenty-first international conference on Machine learning
Exploiting Correlated Attributes in Acquisitional Query Processing
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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Cost-sensitive learning with conditional Markov networks
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Classifying Multiple Imbalanced Attributes in Relational Data
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Adapting cost-sensitive learning for reject option
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Learning from Skewed Class Multi-relational Databases
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
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There has been a recent, growing interest in classification and link prediction in structured domains. Methods such as CRFs (Lafferty et al., 2001) and RMNs (Taskar et al., 2002) 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 a novel cost-sensitive structured classifier based on Maximum Entropy principles that directly determines the cost-sensitive classification. We contrast this with an approach which employs a standard 0/1 loss structured classifier followed by minimization of the expected cost of misclassification. We demonstrate the utility of our proposed classifier with experiments on both synthetic and real-world data.