C4.5: programs for machine learning
C4.5: programs for 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
Learning cost-sensitive active classifiers
Artificial Intelligence
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
"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
A Survey of Uncertain Data Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
DTU: A Decision Tree for Uncertain Data
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A Rule-Based Classification Algorithm for Uncertain Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
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
Decision Trees for Uncertain Data
IEEE Transactions on Knowledge and Data Engineering
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
Cost-Sensitive decision trees with multiple cost scales
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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Uncertainty exists widely in real-word applications. Recently, the research for uncertain data has attracted more and more attention. While not enough attention has been paid to the research of cost- sensitive algorithm on uncertain data. In this paper, we propose a simple but effective method to extend traditional cost-sensitive decision tree to uncertain data, and the algorithm can deal with both certain and uncertain data. In our experiment, we compare the proposed algorithm with DTU[18] on UCI datasets. The experimental result proves that the proposed algorithm performs better than DTU, with lower computational complexity. It keeps low cost even at high level of uncertainty, which makes it applicable to real-life applications for data uncertainty.