Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
The Use of Background Knowledge in Decision Tree Induction
Machine Learning
Qualitative reasoning: modeling and simulation with incomplete knowledge
Qualitative reasoning: modeling and simulation with incomplete knowledge
Qualitative decision theory: from savage's axioms to nonmonotonic reasoning
Journal of the ACM (JACM)
Pruning Improves Heuristic Search for Cost-Sensitive Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Decision Making in Qualitative Influence Diagrams
Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference
Qualitative decision making in adaptive presentation of structured information
ACM Transactions on Information Systems (TOIS)
Test-Cost Sensitive Classification on Data with Missing Values
IEEE Transactions on Knowledge and Data Engineering
Cost-conscious classifier ensembles
Pattern Recognition Letters
Feature value acquisition in testing: a sequential batch test algorithm
ICML '06 Proceedings of the 23rd international conference on Machine learning
Cost-sensitive feature acquisition and classification
Pattern Recognition
On the Choice of Smoothing Parameters for Parzen Estimators of Probability Density Functions
IEEE Transactions on Computers
Test-Cost Sensitive Classification Based on Conditioned Loss Functions
ECML '07 Proceedings of the 18th European conference on Machine Learning
Journal of Artificial Intelligence Research
Generating better decision trees
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Possibility theory as a basis for qualitative decision theory
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Qualitative decision under uncertainty: back to expected utility
Artificial Intelligence
On the foundations of qualitative decision theory
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
From qualitative to quantitative probabilistic networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Active and dynamic information fusion for multisensor systems with dynamic bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper reports a new framework for test-cost sensitive classification. It introduces a new loss function definition, in which misclassification cost and cost of feature extraction are combined qualitatively and the loss is conditioned with current and estimated decisions as well as their consistency. This loss function definition is motivated with the following issues. First, for many applications, the relation between different types of costs can be expressed roughly and usually only in terms of ordinal relations, but not as a precise quantitative number. Second, the redundancy between features can be used to decrease the cost; it is possible not to consider a new feature if it is consistent with the existing ones. In this paper, we show the feasibility of the proposed framework for medical diagnosis problems. Our experiments demonstrate that this framework is efficient to significantly decrease feature extraction cost without decreasing accuracy.