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Test strategies for cost-sensitive decision trees
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-1-4613-8554-7_11openalex-snapshot
Experience with a Model of Sequential Diagnosis
inferredcites →https://doi.org/10.1007/bf00114778openalex-snapshot
The use of background knowledge in decision tree induction
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Cost-Sensitive Learning of Classification Knowledge and Its Applications in Robotics
inferredcites →https://doi.org/10.1007/bfb0094814openalex-snapshot
Inducing cost-sensitive trees via instance weighting
inferredcites →https://doi.org/10.1016/0010-4809(68)90016-5openalex-snapshot
Experience with a model of sequential diagnosis
inferredcites →https://doi.org/10.1023/a:1022609710832openalex-snapshot
The Use of Background Knowledge in Decision Tree Induction
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Active Feature-Value Acquisition for Classifier Induction
inferredcites →https://doi.org/10.1109/icdm.2004.10092openalex-snapshot
Test-Cost Sensitive Naive Bayes Classification
inferredcites →https://doi.org/10.1145/1015330.1015369openalex-snapshot
Decision trees with minimal costs
inferredcites →https://doi.org/10.1145/1089827.1089828openalex-snapshot
Economical active feature-value acquisition through Expected Utility estimation
inferredcites →https://doi.org/10.1145/502512.502540openalex-snapshot
Learning and making decisions when costs and probabilities are both unknown
inferredcites →https://doi.org/10.1613/jair.120openalex-snapshot
Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm
inferredcites →https://doi.org/10.48550/arxiv.cs/0212034openalex-snapshot
Types of Cost in Inductive Concept Learning
What points at this work
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