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Class Noise Handling for Effective Cost-Sensitive Learning by Cost-Guided Iterative Classification Filtering
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/bf00993101openalex-snapshot
Cost-Sensitive Learning of Classification Knowledge and Its Applications in Robotics
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Bridging Local and Global Data Cleansing: Identifying Class Noise in Large, Distributed Data Datasets
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Reducing Misclassification Costs
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inferredcites →https://doi.org/10.1109/icdm.2003.1250950openalex-snapshot
Cost-sensitive learning by cost-proportionate example weighting
inferredcites →https://doi.org/10.1109/icdm.2004.10108openalex-snapshot
Cost-Guided Class Noise Handling for Effective Cost-Sensitive Learning
inferredcites →https://doi.org/10.1109/igarss.1996.516669openalex-snapshot
Improving automated land cover mapping by identifying and eliminating mislabeled observations from training data
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Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm
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Identifying Mislabeled Training Data
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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