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Undersampling of approaching the classification boundary for imbalance problem

https://doi.org/10.1002/cpe.7586
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What this work says

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

inferredcites →https://doi.org/10.1002/cpe.6071openalex-snapshot
CSS : Handling imbalanced data by improved clustering with stratified sampling
inferredcites →https://doi.org/10.1002/cpe.6235openalex-snapshot
Kernel local outlier factor‐based fuzzy support vector machine for imbalanced classification
inferredcites →https://doi.org/10.1002/cpe.6692openalex-snapshot
A Gaussian process‐based approach toward credit risk modeling using stationary activations
inferredcites →https://doi.org/10.1002/sam.11538openalex-snapshot
Imbalanced classification: A paradigm‐based review
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/10.1007/978-981-15-2780-7_99openalex-snapshot
Hybrid Random Under-Sampling Approach in MRI Compressed Sensing
inferredcites →https://doi.org/10.1007/bf00153759openalex-snapshot
Instance-Based Learning Algorithms
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.asoc.2020.107033openalex-snapshot
EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification
inferredcites →https://doi.org/10.1016/j.asoc.2021.107787openalex-snapshot
Boosting the oversampling methods based on differential evolution strategies for imbalanced learning
inferredcites →https://doi.org/10.1016/j.future.2022.01.026openalex-snapshot
Imbalanced data classification: A KNN and generative adversarial networks-based hybrid approach for intrusion detection
inferredcites →https://doi.org/10.1016/j.ins.2014.08.051openalex-snapshot
SMOTE–IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering
inferredcites →https://doi.org/10.1016/j.ins.2017.05.008openalex-snapshot
Clustering-based undersampling in class-imbalanced data
inferredcites →https://doi.org/10.1016/j.ins.2018.06.056openalex-snapshot
Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE
inferredcites →https://doi.org/10.1016/j.ins.2018.10.029openalex-snapshot
Under-sampling class imbalanced datasets by combining clustering analysis and instance selection
inferredcites →https://doi.org/10.1016/j.ins.2020.05.040openalex-snapshot
Resampling ensemble model based on data distribution for imbalanced credit risk evaluation in P2P lending
inferredcites →https://doi.org/10.1016/j.ins.2021.02.069openalex-snapshot
Constructing classifiers for imbalanced data using diversity optimisation
inferredcites →https://doi.org/10.1016/j.knosys.2020.106631openalex-snapshot
On the class overlap problem in imbalanced data classification
inferredcites →https://doi.org/10.1016/j.patcog.2020.107262openalex-snapshot
Radial-Based Undersampling for imbalanced data classification
inferredcites →https://doi.org/10.1016/j.patcog.2021.107965openalex-snapshot
A review of methods for imbalanced multi-label classification
inferredcites →https://doi.org/10.1016/s0031-3203(96)00142-2openalex-snapshot
The use of the area under the ROC curve in the evaluation of machine learning algorithms
inferredcites →https://doi.org/10.1109/18.391248openalex-snapshot
Rates of convergence of nearest neighbor estimation under arbitrary sampling
inferredcites →https://doi.org/10.1109/icife.2010.5609385openalex-snapshot
Cluster-based majority under-sampling approaches for class imbalance learning
inferredcites →https://doi.org/10.1109/icmla.2010.9openalex-snapshot
A Novel Noise Filtering Algorithm for Imbalanced Data
inferredcites →https://doi.org/10.1109/tcyb.2014.2336876openalex-snapshot
Development and Evaluation of Cost-Sensitive Universum-SVM
inferredcites →https://doi.org/10.1109/tcyb.2014.2372060openalex-snapshot
Diversified Sensitivity-Based Undersampling for Imbalance Classification Problems
inferredcites →https://doi.org/10.1109/tcyb.2016.2606104openalex-snapshot
A Noise-Filtered Under-Sampling Scheme for Imbalanced Classification
inferredcites →https://doi.org/10.1109/tcyb.2020.3000754openalex-snapshot
Hashing-Based Undersampling Ensemble for Imbalanced Pattern Classification Problems
inferredcites →https://doi.org/10.1109/tkde.2008.239openalex-snapshot
Learning from Imbalanced Data
inferredcites →https://doi.org/10.1109/tkde.2014.2312336openalex-snapshot
A New Strategy of Cost-Free Learning in the Class Imbalance Problem
inferredcites →https://doi.org/10.1109/tnnls.2019.2913673openalex-snapshot
Radial-Based Oversampling for Multiclass Imbalanced Data Classification
inferredcites →https://doi.org/10.1109/tpami.2012.193openalex-snapshot
Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval
inferredcites →https://doi.org/10.1109/tsmc.2014.2323212openalex-snapshot
Dynamic Bayesian Networks for Fault Detection, Identification, and Recovery in Autonomous Spacecraft
inferredcites →https://doi.org/10.1109/tsmcb.2008.2002909openalex-snapshot
SVMs Modeling for Highly Imbalanced Classification
inferredcites →https://doi.org/10.1109/tsmcb.2012.2227470openalex-snapshot
Weighted Data Gravitation Classification for Standard and Imbalanced Data
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/10.1145/2907070openalex-snapshot
A Survey of Predictive Modeling on Imbalanced Domains
inferredcites →https://doi.org/10.1609/aaai.v33i01.33014707openalex-snapshot
Trainable Undersampling for Class-Imbalance Learning
inferredcites →https://doi.org/10.48550/arxiv.1507.05880openalex-snapshot
A study of the classification of low-dimensional data with supervised manifold learning

What points at this work

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

inferred← citeshttps://doi.org/10.1002/cpe.8103openalex-snapshot
Boosting semi‐supervised learning under imbalanced regression via pseudo‐labeling
inferred← citeshttps://doi.org/10.1016/j.asoc.2025.112819openalex-snapshot
A synergistic oversampling technique with differential evolution and safe level synthetic minority oversampling
inferred← citeshttps://doi.org/10.1016/j.compeleceng.2025.110265openalex-snapshot
Machine learning-inspired intrusion detection system for IoT: Security issues and future challenges
inferred← citeshttps://doi.org/10.1016/j.imu.2026.101731openalex-snapshot
Noise-Aware Undersampling for imbalanced medical data (NAUS)
inferred← citeshttps://doi.org/10.1016/j.ins.2024.121020openalex-snapshot
PCFS: An intelligent imbalanced classification scheme with noisy samples
inferred← citeshttps://doi.org/10.1109/ispacs68724.2025.11382588openalex-snapshot
Prune-Q: a Pruning-Quantization Method for Resource-Constrained Environment Using the Cicids2017 Dataset
inferred← citeshttps://doi.org/10.14569/ijacsa.2024.0150214openalex-snapshot
Hybrid Intrusion Detection System Based on Data Resampling and Deep Learning
inferred← citeshttps://doi.org/10.3390/app14135421openalex-snapshot
An Undersampling Method Approaching the Ideal Classification Boundary for Imbalance Problems
inferred← citeshttps://doi.org/10.3390/pr12061219openalex-snapshot
Machine Learning in Reservoir Engineering: A Review
inferred← citeshttps://doi.org/10.4018/979-8-3373-3541-4.ch004openalex-snapshot
AI-Driven Network Optimization for Sustainable Communication
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