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Boosting trees for cost-sensitive classifications
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/b978-1-55860-335-6.50034-9openalex-snapshot
Reducing Misclassification Costs
inferredcites →https://doi.org/10.1080/00401706.1995.10484383openalex-snapshot
Machine Learning, Neural and Statistical Classification
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Gradient Descent Style Leveraging of Decision Trees and Stumps for Misclassification Cost Performance
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Boosting Cost-Sensitive Trees
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Classifier Loss Under Metric Uncertainty
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Repechage bootstrap aggregating for misclassification cost reduction
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Cost-sensitive ensemble learning: a unifying framework
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A Data Based Model to Predict Landslide Induced by Rainfall in Rio de Janeiro City
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Decision tree classifiers sensitive to heterogeneous costs
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Theory of actionable data mining with application to semiconductor manufacturing control
inferred← citeshttps://doi.org/10.1093/comjnl/bxw015openalex-snapshot
A Cost-Sensitive Decision Tree Learning Algorithm Based on a Multi-Armed Bandit Framework
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Learning Boosted Asymmetric Classifiers for Object Detection
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Cost-sensitive boosting in software quality modeling
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Using Multi-phase Cost-Sensitive Learning to Filtering Spam
inferred← citeshttps://doi.org/10.1109/icdm.2007.95openalex-snapshot
Lazy Bagging for Classifying Imbalanced Data
inferred← citeshttps://doi.org/10.1109/icdmw.2012.33openalex-snapshot
A Multi-armed Bandit Approach to Cost-Sensitive Decision Tree Learning
inferred← citeshttps://doi.org/10.1109/nafips.2007.383813openalex-snapshot
Applying Novel Resampling Strategies To Software Defect Prediction
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Empirical case studies of combining software quality classification models
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Evaluating Stratification Alternatives to Improve Software Defect Prediction
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Floatcascade learning for fast imbalanced web mining
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A survey of cost-sensitive decision tree induction algorithms
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An Empirical evaluation of CostBoost Extensions for Cost-Sensitive Classification
inferred← citeshttps://doi.org/10.1155/2022/9028580openalex-snapshot
Clairvoyant: AdaBoost with Cost-Enabled Cost-Sensitive Classifier for Customer Churn Prediction
inferred← citeshttps://doi.org/10.24251/hicss.2022.191openalex-snapshot
Instance-dependent cost-sensitive learning: do we really need it?
inferred← citeshttps://doi.org/10.2495/data040411openalex-snapshot
A Data Mining Approach To Landslide Prediction
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A pragmatic acceptance of short term policies: the case of floodings in Curitiba, Brasil
inferred← citeshttps://doi.org/10.48550/arxiv.2007.07361openalex-snapshot
Misclassification cost-sensitive ensemble learning: A unifying framework
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Part-of-speech Tagging: A Machine Learning Approach based on Decision Trees
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