The Use of Background Knowledge in Decision Tree Induction
Machine Learning
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
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
Learning cost-sensitive active classifiers
Artificial Intelligence
Inducing Cost-Sensitive Trees via Instance Weighting
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Decision trees with minimal costs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Test-Cost Sensitive Naive Bayes Classification
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Test Strategies for Cost-Sensitive Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Unconstrained Influence Diagram Solver: Guido
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
A comparison of two approaches for solving unconstrained influence diagrams
International Journal of Approximate Reasoning
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Cost-sensitive test strategies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Integrating learning from examples into the search for diagnostic policies
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
On the Complexity of General Solution DAGs
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
Unconstrained influence diagrams
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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In this paper, we deal with an enhanced problem of cost-sensitive classification, where not only the cost of misclassification needs to be minimized, but also the total cost of tests and their requirements. To solve this problem, we propose a novel method CS-UID based on the theory of Unconstrained Influence Diagrams (UIDs). We empirically evaluate and compare CS-UID with an existing algorithm for test-cost sensitive classification (TCSNB) on multiple real-world public referential datasets. We show that CS-UID outperforms TCSNB.