The Use of Background Knowledge in Decision Tree Induction
Machine Learning
C4.5: programs for machine learning
C4.5: programs for 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
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
Active Feature-Value Acquisition for Classifier Induction
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Economical active feature-value acquisition through Expected Utility estimation
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
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
Test Strategies for Cost-Sensitive Decision Trees
IEEE Transactions on Knowledge and Data Engineering
A hierarchical model for test-cost-sensitive decision systems
Information Sciences: an International Journal
Anytime induction of low-cost, low-error classifiers: a sampling-based approach
Journal of Artificial Intelligence Research
Cost-time sensitive decision tree with missing values
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Cost-sensitive classification with respect to waiting cost
Knowledge-Based Systems
Cost sensitive classification in data mining
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Decision tree classifiers sensitive to heterogeneous costs
Journal of Systems and Software
Cost-sensitive classification with unconstrained influence diagrams
SOFSEM'12 Proceedings of the 38th international conference on Current Trends in Theory and Practice of Computer Science
The CASH algorithm-cost-sensitive attribute selection using histograms
Information Sciences: an International Journal
A cost-sensitive decision tree approach for fraud detection
Expert Systems with Applications: An International Journal
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In medical diagnosis doctors must often determine what medical tests (e.g., X-ray, blood tests) should be ordered for a patient to minimize the total cost of medical tests and misdiagnosis. In this paper, we design cost-sensitive machine learning algorithms to model this learning and diagnosis process. Medical tests are like attributes in machine learning whose values may be obtained at cost (attribute cost), and misdiagnoses are like misclassifications which may also incur a cost (misclassification cost). We first propose an improved decision tree learning algorithm that minimizes the sum of attribute costs and misclassification costs. Then we design several novel "test strategies" that may request to obtain values of unknown attributes at cost (similar to doctors' ordering of medical tests at cost) in order to minimize the total cost for test examples (new patients). We empirically evaluate and compare these test strategies, and show that they are effective and that they outperform previous methods. A case study on heart disease is given.