The Use of Background Knowledge in Decision Tree Induction
Machine Learning
Learning cost-sensitive active classifiers
Artificial Intelligence
Pruning Improves Heuristic Search for Cost-Sensitive Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Test-Cost Sensitive Classification on Data with Missing Values
IEEE Transactions on Knowledge and Data Engineering
Cost-conscious classifier ensembles
Pattern Recognition Letters
Cost-sensitive feature acquisition and classification
Pattern Recognition
Journal of Artificial Intelligence Research
Generating better decision trees
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Cost-Sensitive decision tree learning for forensic classification
ECML'06 Proceedings of the 17th European conference on Machine Learning
Active and dynamic information fusion for multisensor systems with dynamic bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Qualitative test-cost sensitive classification
Pattern Recognition Letters
Test-cost sensitive classification using greedy algorithm on training data
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
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We report a novel approach for designing test-cost sensitive classifiers that consider the misclassification cost together with the cost of feature extraction utilizing the consistency behavior for the first time. In this approach, we propose to use a new Bayesian decision theoretical framework in which the loss is conditioned with the current decision and the expected decisions after additional features are extracted as well as the consistency among the current and expected decisions. This approach allows us to force the feature extraction for samples for which the current and expected decisions are inconsistent. On the other hand, it forces not to extract any features in the case of consistency, leading to less costly but equally accurate decisions. In this work, we apply this approach to a medical diagnosis problem and demonstrate that it reduces the overall feature extraction cost up to 47.61 percent without decreasing the accuracy.