The Use of Background Knowledge in Decision Tree Induction
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Medical Data Mining and Knowledge Discovery
Medical Data Mining and Knowledge Discovery
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
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
ICML '00 Proceedings of the Seventeenth 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
Attribute Measurement Policies for Time and Cost Sensitive Classification
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
"Missing Is Useful': Missing Values in Cost-Sensitive Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Test Strategies for Cost-Sensitive Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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
Hybrid cost-sensitive decision tree
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Simple test strategies for cost-sensitive decision trees
ECML'05 Proceedings of the 16th European conference on Machine Learning
Decision tree classifiers sensitive to heterogeneous costs
Journal of Systems and Software
The CASH algorithm-cost-sensitive attribute selection using histograms
Information Sciences: an International Journal
A survey of cost-sensitive decision tree induction algorithms
ACM Computing Surveys (CSUR)
Decision trees: a recent overview
Artificial Intelligence Review
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Classification plays an important role in medicine, especially for medical diagnosis. Health applications often require classifiers that minimize the total cost, including misclassifications costs and test costs. In fact, there are many reasons for considering costs in medicine, as diagnostic tests are not free and health budgets are limited. Our aim with this work was to define, implement and test a strategy for cost-sensitive learning. We defined an algorithm for decision tree induction that considers costs, including test costs, delayed costs and costs associated with risk. Then we applied our strategy to train and evaluate cost-sensitive decision trees in medical data. Built trees can be tested following some strategies, including group costs, common costs, and individual costs. Using the factor of "risk" it is possible to penalize invasive or delayed tests and obtain decision trees patient-friendly.