Proceedings of the sixth international workshop on Machine learning
Cost-sensitive concept learning of sensor use in approach and recognition
Proceedings of the sixth international workshop on Machine learning
Incremental Induction of Decision Trees
Machine Learning
Machine Learning
Noise-tolerant instance-based learning algorithms
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
ACE-Cost: acquisition cost efficient classifier by hybrid decision tree with local SVM leaves
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Paper: A support for decision-making: Cost-sensitive learning system
Artificial Intelligence in Medicine
An ensemble-clustering-based distance metric and its applications
International Journal of Business Intelligence and Data Mining
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This paper explores the problem of learning from examples when feature measurement costs are significant. It then extends two effective and familiar learning methods, ID3 and IBL, to address this problem. The extensions, CS-ID3 and CS-IBL, are described in detail and are tested in a natural robot domain and a synthetic domain. Empirical studies support the hypothesis that the extended methods are indeed sensitive to feature costs: they deal effectively with varying cost distributions and with irrelevant features.