INGB improves oversampling for noisy imbalanced datasets.
problem Imbalanced, noisy, and complex datasets in classification problems.
method INGB uses granular balls to simulate spatial distribution and informed entropy for optimization, followed by nonlinear oversampling.
result INGB outperforms traditional linear sampling frameworks and algorithms on complex datasets.
A simple oversampling method improves classification results for imbalanced datasets.
problem Learning from class-imbalanced data in supervised learning.
method k-means clustering and SMOTE oversampling.
result Training data oversampled with the proposed method improves classification results.
A new method improves fault diagnostics and prognostics for class-imbalanced data.
problem Class imbalance in industrial fault diagnostics and prognostics.
method EWMOTE: EM-based Weighted Minority Oversampling TEchnique.
result EWMOTE achieves better performance on binary and multi-class imbalance learning tasks.
A new oversampling framework generates minority samples by perturbing majority classes.
problem Oversampling in imbalanced classification often neglects majority classes, leading to samples spread across the minority space.
method Introduces a counterfactual objective to generate new minority samples by perturbing majority samples.
result Generated minority samples are near the decision boundary and significantly outperform state-of-the-art methods.
This paper tackles imbalanced classification with weakly supervised oversampling.
problem Imbalanced classification in high-dimensional datasets.
method Weakly supervised SMOTE, cost-sensitive NCA, bootstrap ensemble.
result Improved classification performance on synthetic and real-world datasets.
GenSample uses genetic algorithms to improve minority class classification in imbalanced datasets.
problem Poor classification performance on minority class in imbalanced datasets.
method GenSample uses genetic algorithms to oversample minority class, considering difficulty and performance improvement.
result GenSample achieved the highest F-Score on 8 out of 9 real-world imbalanced datasets.
Proposes a new technique for handling imbalanced data.
problem Data imbalance in classification tasks.
method Combines oversampling and undersampling techniques.
result CSMOUTE shows promise for handling imbalanced datasets.
LoRAS improves model performance on imbalanced datasets by better oversampling the minority class.
problem Imbalanced datasets lead to poor model performance, especially for the majority class.
method Localized Random Affine Shadowsampling (LoRAS) to oversample minority class data.
result LoRAS generates better ML models in terms of F1-Score and Balanced accuracy compared to SMOTE and its extensions.
Paper proposes Adversarial Oversampling for heart disease detection.
problem Imbalanced classes in heartbeats images classification.
method 2D Convolutional Neural Network with InfoGAN for synthetic oversampling.
result Proposed Adversarial Oversampling improves classifier performance for minority classes.
Proposes GMOTE for better handling imbalanced data.
problem Classification of imbalanced data.
method Generates minority class instances using Gaussian Mixture Model, considering outliers through Mahalanobis distance.
result Shows better accuracy and F1-Score when combined with CART or SVM.
We improve kernel ridge regression for skewed responses using oversampling and adaptive partitioning.
problem Kernel ridge regression struggles with skewed response variables, leading to poor estimates.
method Combines adaptive partitioning with oversampling to address skewed responses in kernel ridge regression.
result The proposed method yields estimates with smaller risk compared to classical methods under mild conditions.
Paper tackles imbalanced time series classification with a novel oversampling method.
problem Imbalanced time series classification challenges due to high dimensionality and correlation.
method Density-ratio based clustering followed by shrinkage technique for covariance estimation, then generating synthetic samples.
result OHIT outperforms state-of-the-art methods in F1, G-mean, and AUC metrics.
GAT-RWOS uses graph attention to improve imbalanced data classification.
problem Imbalanced data leads to biased models favoring majority classes.
method Combines GATs and random walks to generate synthetic minority samples.
result Improves classification performance on imbalanced datasets.
Paper proposes a DAE algorithm to improve credit card fraud detection.
problem Imbalanced data classification problem in credit card fraud detection.
method Proposes a denoising autoencoder neural network (DAE) algorithm to oversample and denoise minority class samples.
result Improves classification accuracy of minority class samples in imbalanced datasets.
The paper explores how deep generative models can improve performance on imbalanced datasets.
problem Imbalanced datasets lead to poor classifier performance and misleading evaluation metrics.
method The paper studies deep generative models for oversampling minority classes in imbalanced tabular data.
result Deep generative models can improve performance on imbalanced classification tasks, but require different under- and oversampling ratios.
WOTBoost improves minority class accuracy in imbalanced datasets.
problem Imbalanced datasets lead to low accuracy in minority class classification.
method Combines weighted oversampling and boosting techniques.
result WOTBoost achieves best G mean and highest AUC score on multiple datasets.
This paper uses LLMs to generate synthetic data to improve classification accuracy in imbalanced datasets.
problem Imbalanced classification and spurious correlation in data science.
method Develops novel theoretical foundations and uses transformer models to generate synthetic data.
result Transformer models can generate high-quality synthetic data to improve classification accuracy.
Develops a new synthetic minority oversampling technique for imbalanced learning.
problem Imbalanced learning in classification models.
method Generates synthetic samples using Gaussian Mixture Model in high-dimensional space, filters outliers, and optimizes parameters.
result An effective and efficient imbalanced learning framework is developed.
This chapter tackles class imbalance in datasets to promote data democracy.
problem Class imbalance in datasets leading to biased decisions and policies.
method Statistical measures and data-level methods (oversampling, undersampling, etc.) applied to a real dataset.
result Popular data-level methods improve performance in handling class imbalance.
LDAO addresses imbalanced regression by learning local distribution structures.
problem Imbalanced regression with sparse target regions difficult for models.
method LDAO learns local distribution structures, models and samples from each, then merges.
result LDAO outperforms state-of-the-art methods on 45 imbalanced datasets.
The paper addresses frequency-dependent distortions in massive MIMO systems and proposes a method to recover covariance matrices.
problem Frequency-dependent distortions in the covariance matrix of massive MIMO systems.
method Proposes a novel UL-DL covariance interpolation technique under a mild reciprocity condition.
result The proposed method can recover the covariance matrix in the DL from an estimate in the UL, especially in FDD massive MIMO systems.
Proposes a new sampling method for online learning with cumulative oversampling.
problem Budgeted Influence Maximization in online learning.
method Cumulative Oversampling (CO) method for online learning.
result CO-based algorithm achieves comparable regret to UCB-based algorithms and performs similarly to Thompson Sampling.
A new method for synthetic oversampling of multi-label data focusing on local label distribution.
problem Class imbalance in multi-label datasets affects prediction accuracy.
method Proposes a new method for synthetic oversampling of multi-label data focusing on local label distribution.
result Demonstrates effectiveness in generating more diverse and better labeled instances.
Optimal model improves AUC, recall, and F1 score for class-imbalanced business risk.
problem Improving prediction of class-imbalanced business risk.
method Resampling, regularization, and model ensembling techniques.
result Boosting on DT with SMOTE oversampling achieves AUC, recall, and F1 score of 0.8633, 0.9260, and 0.8907, respectively.
Generative data augmentation improves unsupervised anomaly detection.
problem Improving anomaly detection performance in unsupervised settings.
method Oversampling infrequent normal samples using adversarial autoencoder (AAE) to transform high-dimensional multimodal data into low-dimensional unimodal latent distributions.
result Consistent improvements in anomaly detection across various real-world datasets.
OSTSC improves RNN performance on imbalanced time series data.
problem Improving classification accuracy on imbalanced time series data.
method Over-sampling approach for time series classification in R.
result OSTSC increases AUC from 0.543 to 0.784 on high frequency trading dataset.
Study investigates class imbalance in CNNs and compares methods to address it.
problem Class imbalance in convolutional neural networks (CNNs).
method Three benchmark datasets (MNIST, CIFAR-10, ImageNet) and comparison of oversampling, undersampling, two-phase training, and thresholding.
result Oversampling is the dominant method for addressing class imbalance in CNNs.
This paper tackles imbalanced data in binary classification problems.
problem Imbalanced data leads to skewed results in classification problems.
method Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) Sampling Approach.
result Synthetic data points enhance understanding of oversampling techniques.
A new method VOS uses variational autoencoders to balance imbalanced datasets.
problem Class imbalance in datasets leads to skewed predictions and false positives.
method Inspired by variational inference and GANs, VOS creates synthetic minority class samples.
result VOS outperforms traditional oversampling methods in augmenting imbalanced datasets.
A new LLM-based method enhances diversity in oversampling for imbalanced classification.
problem Limited diversity in synthetic minority samples generated by current LLM-based approaches reduces robustness and generalizability.
method Condition synthetic sample generation on minority labels and features, use permutation strategy for fine-tuning, fine-tune on minority and interpolated samples.
result Significantly outperforms eight SOTA baselines in diverse synthetic sample generation and downstream classification tasks.
AnyThreat detects insider threats with minimal false positives.
problem High false positives in detecting insider threats.
method Opportunistic knowledge discovery system with four components: feature engineering, oversampling, class decomposition, and classification.
result Detects 87.5% of malicious insider threats with minimal false positives.
The paper analyzes SMOTE for imbalanced classification, providing theoretical bounds and guidelines.
problem The challenge of imbalanced classification problems, especially with minority classes.
method Theoretical analysis of SMOTE and related oversampling techniques for minority classes.
result Derives concentration and excess risk bounds for SMOTE and kernel-based classifiers.
QPCA improves PCA for cyclostationary data.
problem Improving PCA for cyclostationary data.
method Formulated as an optimization problem, QPCA decomposes into frequency-domain PCA problems.
result Optimized basis for cyclostationary data.
Paper tackles spam filtering on forums using synthetic oversampling.
problem Imbalanced data in forums leads to poor spam detection.
method Synthetic Minority Over-sampling Technique (SMOTE) to balance data.
result Models trained with SMOTE outperform those trained on imbalanced data.
This paper surveys various data balancing methods for imbalanced datasets.
problem Imbalanced datasets bias predictions and degrade classifier performance.
method Extensive review of oversampling, undersampling, adaptive, generative, combination, and ensemble methods.
result No single method universally outperforms others; selection depends on dataset characteristics.
Paper develops consistent estimation of propensity scores for rare exposures.
problem Estimation of propensity score functions for rare exposures in oversampled cohorts.
method Flexible computational implementation using source population probability of exposure and observation weighting.
result Low empirical bias and variance for consistent propensity score function estimators.
Paper tackles gene mutation prediction for HCC using multi-instance multi-label learning.
problem Gene mutation prediction in hepatocellular carcinoma for personalized treatments.
method Multi-instance multi-label learning with oversampling for data imbalance.
result Proposed approach shows superiority in gene mutation prediction.
New undersampling method reduces computational complexity for imbalanced datasets.
problem Data imbalance in machine learning datasets.
method Radial-Based Undersampling (RBO) using mutual class potential.
result Significantly reduced time complexity of the proposed algorithm.
Synthetic sampling improves per-class performance in multi-label malignancy prediction.
problem Imbalanced multi-label classification problem in CADx systems.
method Synthetic oversampling techniques using random forest classifier.
result Average 7.22% point increase in sensitivity for minority classes.
Study examines how event rate affects bankruptcy prediction model performance.
problem Effect of event rate on bankruptcy prediction model performance.
method Oversampled event rates from 0.12% to 50%, developed and evaluated 7 models.
result Bayesian Network is least sensitive to event rate, SVM most sensitive.
A novel resampling technique addresses class imbalance in imbalanced datasets.
problem Class imbalance in real-world datasets, especially in rare event detection.
method Developed two oversampling algorithms: G1Nos 1-Nearest Neighbour.
result Our oversampling algorithms outperform state-of-the-art methods in all metrics.
A new algorithm enhances minority class representation in imbalanced datasets.
problem Improving classification performance on imbalanced datasets.
method PO-QG algorithm using Proxima-Orion neighbors and q-Gaussian weighting.
result The PO-QG algorithm improves overall classification performance.
A novel two-stage resampling method improves CNN training on imbalanced colorectal cancer image data.
problem Data imbalance in medical image datasets, especially in histopathological images.
method Two-stage resampling: first oversampling in image space, then undersampling in feature space.
result The proposed method enhances CNN training on imbalanced colorectal cancer image datasets.
Proposes a new undersampling method for imbalanced data classification.
problem Challenges of oversampling and undersampling in imbalanced data.
method Bilevel optimization framework for identifying optimal subset of majority training data.
result Improves F1 scores by up to 10% compared to state-of-the-art methods.
A new algorithm reduces imbalanced data classification errors in multi-class settings.
problem Imbalanced data classification, especially with noise and overlapping classes.
method MC-CCR algorithm combining cleaning and resampling.
result High robustness to noise and superior performance compared to state-of-the-art methods.
The paper addresses classification imbalance by framing it as a transfer learning problem.
problem Classification imbalance where one class is much rarer than the other.
method The paper studies oversampling procedures to balance classes, focusing on SMOTE and bootstrapping.
result The excess risk decomposes into balanced training rate and transfer cost, with SMOTE having a higher transfer cost.
Proposes a new data augmentation method for imbalanced datasets in both classification and regression.
problem Imbalanced datasets in supervised learning, especially in regression.
method GOLIATH algorithm based on kernel density estimates for classification and regression.
result Significant improvement over existing state-of-the-art techniques in imbalanced regression.
Framework learns to transform majority to minority samples for balanced classification.
problem Imbalanced classification leading to biased predictions.
method Minimizes MMD and uses triplet loss for global alignment and boundary awareness.
result Consistent improvements over classical and generative baselines in AUROC, G-mean, F1-score, and MCC.