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Imbalanced classification: A paradigm‐based review

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
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A Comparative Study for Email Classification
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An Introduction to Statistical Learning
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Learning Ensembles of First-Order Clauses for Recall-Precision Curves: A Case Study in Biomedical Information Extraction
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Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems
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Nonparametric and Probabilistic Classification Using NN-balls with Environmental and Remote Sensing Applications
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Pruning decision trees with misclassification costs
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Boosting support vector machines for imbalanced data sets
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Effective detection of sophisticated online banking fraud on extremely imbalanced data
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An empirical study of a hybrid imbalanced-class DT-RST classification procedure to elucidate therapeutic effects in uremia patients
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Threshold optimization for classification in imbalanced data in a problem of gamma-ray astronomy
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Surrounding neighborhood-based SMOTE for learning from imbalanced data sets
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Undersampled $$K$$ K -means approach for handling imbalanced distributed data
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FORGE Canada Consortium: Outcomes of a 2-Year National Rare-Disease Gene-Discovery Project
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Finding the Best Classification Threshold in Imbalanced Classification
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Ensemble-based hybrid probabilistic sampling for imbalanced data learning in lung nodule CAD
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Efficient surface finish defect detection using reduced rank spline smoothers and probabilistic classifiers
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Cluster-based under-sampling approaches for imbalanced data distributions
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Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics
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Adaptive semi-unsupervised weighted oversampling (A-SUWO) for imbalanced datasets
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Learning from class-imbalanced data: Review of methods and applications
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Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data
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An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
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SMOTE–IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering
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On the effectiveness of preprocessing methods when dealing with different levels of class imbalance
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Random Balance: Ensembles of variable priors classifiers for imbalanced data
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A method for resampling imbalanced datasets in binary classification tasks for real-world problems
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Cost-sensitive boosting for classification of imbalanced data
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A novel ensemble method for classifying imbalanced data
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The use of the area under the ROC curve in the evaluation of machine learning algorithms
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Machine Learning for the Detection of Oil Spills in Satellite Radar Images
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Random Forests
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Support Vector Machines for Classification in Nonstandard Situations
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An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression
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A survey of decision tree classifier methodology
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An imbalanced data classification algorithm of improved autoencoder neural network
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Cost-sensitive learning by cost-proportionate example weighting
inferredcites →https://doi.org/10.1109/ijcnn.2008.4633969openalex-snapshot
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
inferredcites →https://doi.org/10.1109/tkde.2005.50openalex-snapshot
Using AUC and accuracy in evaluating learning algorithms
inferredcites →https://doi.org/10.1109/tkde.2006.17openalex-snapshot
Training cost-sensitive neural networks with methods addressing the class imbalance problem
inferredcites →https://doi.org/10.1109/tnnls.2013.2246188openalex-snapshot
Novel Cost-Sensitive Approach to Improve the Multilayer Perceptron Performance on Imbalanced Data
inferredcites →https://doi.org/10.1111/j.1467-985x.2005.00368_10.xopenalex-snapshot
Discriminant Analysis and Statistical Pattern Recognition
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Adaptive Weighted Learning for Unbalanced Multicategory Classification
inferredcites →https://doi.org/10.1126/sciadv.aao1659openalex-snapshot
Neyman-Pearson classification algorithms and NP receiver operating characteristics
inferredcites →https://doi.org/10.1142/s0218001409007326openalex-snapshot
CLASSIFICATION OF IMBALANCED DATA: A REVIEW
inferredcites →https://doi.org/10.1142/s021964922040016xopenalex-snapshot
A Comparison of Resampling Techniques for Medical Data Using Machine Learning
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Feature selection for text categorization on imbalanced data
inferredcites →https://doi.org/10.1145/1015330.1015369openalex-snapshot
Decision trees with minimal costs
inferredcites →https://doi.org/10.1145/1143844.1143874openalex-snapshot
The relationship between Precision-Recall and ROC curves
inferredcites →https://doi.org/10.1145/2939672.2939785openalex-snapshot
XGBoost
inferredcites →https://doi.org/10.1145/312129.312220openalex-snapshot
MetaCost
inferredcites →https://doi.org/10.1198/jasa.2010.tm08487openalex-snapshot
Weighted Distance Weighted Discrimination and Its Asymptotic Properties
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/10.1613/jair.953openalex-snapshot
SMOTE: Synthetic Minority Over-sampling Technique
inferredcites →https://doi.org/10.21236/ada164453openalex-snapshot
Learning Internal Representations by Error Propagation
inferredcites →https://doi.org/10.2307/2344614openalex-snapshot
Generalized Linear Models
inferredcites →https://doi.org/10.48550/arxiv.1102.5750openalex-snapshot
Neyman-Pearson classification, convexity and stochastic constraints
inferredcites →https://doi.org/10.5539/mas.v14n7p92openalex-snapshot
Analysis of Resampling Techniques on Predictive Performance of Credit Card Classification
inferredcites →https://doi.org/10.5555/1248659.1248686openalex-snapshot
Infinitely Imbalanced Logistic Regression

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Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Additional file 1 of Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion
inferred← citeshttps://doi.org/10.6084/m9.figshare.26723873openalex-snapshot
Additional file 2 of Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion
inferred← citeshttps://doi.org/10.6084/m9.figshare.26723876openalex-snapshot
Additional file 3 of Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion
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Additional file 4 of Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion
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Additional file 5 of Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion
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Additional file 6 of Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion
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Additional file 7 of Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion
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Additional file 8 of Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion
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Additional file 9 of Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion
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Additional file 10 of Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion
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Additional file 11 of Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion
inferred← citeshttps://doi.org/10.6084/m9.figshare.26723903openalex-snapshot
Additional file 12 of Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion
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