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Entropy‐based hybrid sampling ensemble learning for imbalanced data

https://doi.org/10.1002/int.22388
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What this work says

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

inferredcites →https://doi.org/10.1007/11538059_91openalex-snapshot
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning
inferredcites →https://doi.org/10.1007/11893028_93openalex-snapshot
EUS SVMs: Ensemble of Under-Sampled SVMs for Data Imbalance Problems
inferredcites →https://doi.org/10.1007/978-0-387-73003-5_299openalex-snapshot
Support Vector Machine
inferredcites →https://doi.org/10.1007/978-3-319-31863-9_5openalex-snapshot
Stratified Over-Sampling Bagging Method for Random Forests on Imbalanced Data
inferredcites →https://doi.org/10.1007/978-3-540-24680-0_88openalex-snapshot
An OGSA-Based Quality of Service Framework
inferredcites →https://doi.org/10.1007/978-3-642-39479-9_47openalex-snapshot
Evaluation of Sampling Methods for Learning from Imbalanced Data
inferredcites →https://doi.org/10.1007/s00500-008-0323-yopenalex-snapshot
KEEL: a software tool to assess evolutionary algorithms for data mining problems
inferredcites →https://doi.org/10.1007/s00500-014-1291-zopenalex-snapshot
To combat multi-class imbalanced problems by means of over-sampling and boosting techniques
inferredcites →https://doi.org/10.1007/s10462-009-9124-7openalex-snapshot
Ensemble-based classifiers
inferredcites →https://doi.org/10.1007/s10489-011-0287-yopenalex-snapshot
DBSMOTE: Density-Based Synthetic Minority Over-sampling TEchnique
inferredcites →https://doi.org/10.1007/s11704-016-5250-yopenalex-snapshot
Boosting imbalanced data learning with Wiener process oversampling
inferredcites →https://doi.org/10.1007/s13748-016-0094-0openalex-snapshot
Learning from imbalanced data: open challenges and future directions
inferredcites →https://doi.org/10.1016/j.engappai.2015.09.011openalex-snapshot
BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification
inferredcites →https://doi.org/10.1016/j.engappai.2016.10.008openalex-snapshot
Feature selection for high dimensional imbalanced class data using harmony search
inferredcites →https://doi.org/10.1016/j.eswa.2016.12.035openalex-snapshot
Learning from class-imbalanced data: Review of methods and applications
inferredcites →https://doi.org/10.1016/j.eswa.2018.01.023openalex-snapshot
Progressive boosting for class imbalance and its application to face re-identification
inferredcites →https://doi.org/10.1016/j.ins.2013.12.019openalex-snapshot
Clustering-based ensembles for one-class classification
inferredcites →https://doi.org/10.1016/j.jksuci.2012.05.001openalex-snapshot
Entropy based fuzzy classification of images on quality assessment
inferredcites →https://doi.org/10.1016/j.knosys.2015.09.015openalex-snapshot
Gravitational fixed radius nearest neighbor for imbalanced problem
inferredcites →https://doi.org/10.1016/j.knosys.2015.10.020openalex-snapshot
Multi-granular mining for boundary regions in three-way decision theory
inferredcites →https://doi.org/10.1016/j.neucom.2014.06.021openalex-snapshot
A resampling ensemble algorithm for classification of imbalance problems
inferredcites →https://doi.org/10.1016/j.neunet.2019.06.003openalex-snapshot
Cascade interpolation learning with double subspaces and confidence disturbance for imbalanced problems
inferredcites →https://doi.org/10.1017/s026988891300043xopenalex-snapshot
One-class classification: taxonomy of study and review of techniques
inferredcites →https://doi.org/10.1109/fskd.2008.407openalex-snapshot
A Hybrid Re-sampling Method for SVM Learning from Imbalanced Data Sets
inferredcites →https://doi.org/10.1109/ijcnn.2008.4633969openalex-snapshot
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
inferredcites →https://doi.org/10.1109/iri.2008.4583030openalex-snapshot
Hybrid sampling for imbalanced data
inferredcites →https://doi.org/10.1109/mmcs.1999.779220openalex-snapshot
CPU service classes for multimedia applications
inferredcites →https://doi.org/10.1109/tcyb.2018.2877663openalex-snapshot
Geometric Structural Ensemble Learning for Imbalanced Problems
inferredcites →https://doi.org/10.1109/tkde.2008.239openalex-snapshot
Learning from Imbalanced Data
inferredcites →https://doi.org/10.1109/tkde.2012.232openalex-snapshot
MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning
inferredcites →https://doi.org/10.1109/tkde.2016.2535283openalex-snapshot
Large Margin Distribution Learning with Cost Interval and Unlabeled Data
inferredcites →https://doi.org/10.1109/tnnls.2017.2676239openalex-snapshot
Boundary-Eliminated Pseudoinverse Linear Discriminant for Imbalanced Problems
inferredcites →https://doi.org/10.1109/tpami.2006.134openalex-snapshot
Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval
inferredcites →https://doi.org/10.1109/tpami.2010.188openalex-snapshot
Selecting Critical Patterns Based on Local Geometrical and Statistical Information
inferredcites →https://doi.org/10.1109/tsmcb.2008.2007853openalex-snapshot
Exploratory Undersampling for Class-Imbalance Learning
inferredcites →https://doi.org/10.1109/tsmcc.2011.2161285openalex-snapshot
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
inferredcites →https://doi.org/10.1111/j.1467-8640.2010.00358.xopenalex-snapshot
ON MULTI‐CLASS COST‐SENSITIVE LEARNING
inferredcites →https://doi.org/10.1137/1.9781611972788.13openalex-snapshot
Roughly Balanced Bagging for Imbalanced Data
inferredcites →https://doi.org/10.1504/ijaip.2017.081179openalex-snapshot
Over-sampling via under-sampling in strongly imbalanced data
inferredcites →https://doi.org/10.1613/jair.953openalex-snapshot
SMOTE: Synthetic Minority Over-sampling Technique
inferredcites →https://doi.org/10.3233/his-160226openalex-snapshot
Sampling based hybrid algorithms for imbalanced data classification

What points at this work

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

inferred← citeshttps://doi.org/10.1002/int.22814openalex-snapshot
Semisupervised image classification by mutual learning of multiple self‐supervised models
inferred← citeshttps://doi.org/10.1007/978-3-031-08751-6_14openalex-snapshot
Hierarchical Ensemble Based Imbalance Classification
inferred← citeshttps://doi.org/10.1007/s00521-023-09180-xopenalex-snapshot
VGAN-BL: imbalanced data classification based on generative adversarial network and biased loss
inferred← citeshttps://doi.org/10.1007/s10115-025-02377-7openalex-snapshot
CTVAE: Contrastive Tabular Variational Autoencoder for imbalance data
inferred← citeshttps://doi.org/10.1007/s10489-025-06460-yopenalex-snapshot
Radial-based oversampling based on differential evolution for imbalanced data
inferred← citeshttps://doi.org/10.1007/s11227-022-04851-3openalex-snapshot
KNNGAN: an oversampling technique for textual imbalanced datasets
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Adaptive confidence-weighted regularization for enhancing CNN performance on imbalanced datasets
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A clustering and generative adversarial networks-based hybrid approach for imbalanced data classification
inferred← citeshttps://doi.org/10.1007/s13042-023-01977-5openalex-snapshot
Incremental feature selection based on uncertainty measure for dynamic interval-valued data
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Advanced R-GAN: Generating anomaly data for improved detection in imbalanced datasets using regularized generative adversarial networks
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Improving unbalanced image classification through fine-tuning method of reinforcement learning
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Leveraging GANs data augmentation for imbalanced medical image classification
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Transient stability assessment with time-adaptive method based on spatial distribution
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RVGAN-TL: A generative adversarial networks and transfer learning-based hybrid approach for imbalanced data classification
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RGAN-EL: A GAN and ensemble learning-based hybrid approach for imbalanced data classification
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DEEL: An imbalanced binary data classification method based on diffusion model data augmentation and multi-objective optimization ensemble
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Sensitivity-dependent dynamic searching approach coupling multi-intelligent surrogates in homotopy mechanism for groundwater DNAPL-source inversion
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Evolving ensembles using multi-objective genetic programming for imbalanced classification
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A hybrid multi-criteria meta-learner based classifier for imbalanced data
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An improved SMOTE algorithm for enhanced imbalanced data classification by expanding sample generation space
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Machine Learning Based Method for Insurance Fraud Detection on Class Imbalance Datasets With Missing Values
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Evolutionary-Based Multi-Objective and Conditional Generative Adversarial Networks for Credit Scoring
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E-SMOTE: Entropy Based Minority Oversampling for Heart Failure and AIDS Clinical Trails Analysis
inferred← citeshttps://doi.org/10.1109/eict61409.2023.10427712openalex-snapshot
A Hybrid Preprocessing Approach for the Classification of Class Imbalanced Data
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Prune-Q: a Pruning-Quantization Method for Resource-Constrained Environment Using the Cicids2017 Dataset
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Role of Machine and Deep Learning in Predicting Protein Modification Sites: Review and Future Directions
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Divide-and-Merge Parallel Hierarchical Ensemble DNNs with Local Knowledge Augmentation
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Recognition of Air Passengers' Willingness to Pay for Seat Selection for Imbalanced Data Based on Improved XGBoost
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