Cost-sensitive pruning of decision trees
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
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
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Text Categorization Based on Regularized Linear Classification Methods
Information Retrieval
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Pruning Decision Trees with Misclassification Costs
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Expectation Propagation for approximate Bayesian inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Combining Statistical and Relational Methods for Learning in Hypertext Domains
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Learning probabilistic models of link structure
The Journal of Machine Learning Research
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An iterative method for multi-class cost-sensitive learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning associative Markov networks
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
Intelligent light control using sensor networks
Proceedings of the 3rd international conference on Embedded networked sensor systems
Cost-sensitive learning with conditional Markov networks
ICML '06 Proceedings of the 23rd international conference on Machine learning
Discriminative unsupervised learning of structured predictors
ICML '06 Proceedings of the 23rd international conference on Machine learning
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
Turbo decoding as an instance of Pearl's “belief propagation” algorithm
IEEE Journal on Selected Areas in Communications
Iterative decoding of compound codes by probability propagation in graphical models
IEEE Journal on Selected Areas in Communications
Guest editorial: special issue on utility-based data mining
Data Mining and Knowledge Discovery
Large margin cost-sensitive learning of conditional random fields
Pattern Recognition
The echo state conditional random field model for sequential data modeling
Expert Systems with Applications: An International Journal
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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.