UBMF tackles fault diagnosis in imbalanced industrial data with enhanced accuracy and adaptability.
problem Fault diagnosis challenges in imbalanced industrial data.
method Integrates four key modules: data perturbation, cross-task feature extraction, uncertainty-based filtering, and Bayesian meta-knowledge integration.
result Achieves an average improvement of 42.22% across ten diagnostic tasks.
Study investigates how preprocessing, feature selection, and model selection affect performance on imbalanced genetic data.
problem Challenges in using machine learning on imbalanced genetic datasets.
method Comparative analysis of data preprocessing, feature selection techniques, and machine learning models on imbalanced genetic data.
result Class-imbalanced target variables and skewed predictors have little to no impact on classification performance.
Develops a new minimax probability machine for imbalanced classification tasks.
problem Imbalanced classification tasks with non-decomposable performance measures.
method Derives an equivalent form of the MPMF model for solving linear and nonlinear classifiers.
result Demonstrates the effectiveness of the new model on real-world datasets.
Bayesian TAML balances meta-knowledge and task-specific learning for imbalanced tasks.
problem Meta-learning approaches struggle with varying instance and class numbers and distributional differences.
method Bayesian inference framework with variational inference to adaptively balance meta-knowledge and task-specific learning.
result Bayesian TAML significantly outperforms existing meta-learning approaches on imbalanced datasets.
New metric learning algorithm for imbalanced data.
problem Learning metrics in imbalanced datasets.
method Designing a new Mahalanobis metric learning algorithm (IML) for class imbalance.
result IML shows efficiency in learning metrics for imbalanced data.
DAMVI algorithm improves imbalanced binary classification by adjusting weights of examples and classifiers.
problem Imbalanced binary classification tasks where minority class is underrepresented.
method DAMVI algorithm increases positive example weights and optimizes classifier weights using PAC-Bayesian C-Bound.
result DAMVI outperforms state-of-the-art models on various imbalanced datasets.
Paper studies SERA's effectiveness in optimizing imbalanced regression models.
problem Imbalanced regression tasks where extreme values are crucial.
method Gradient boosting algorithms tested with 36 datasets.
result Models using SERA as objective function perform better at extreme value predictions.
BERT improved for propaganda detection with imbalanced, dissimilar data.
problem BERT struggles with dissimilar imbalanced datasets in propaganda detection.
method Cost-sensitive BERT with dissimilarity measure for imbalanced, dissimilar datasets.
result Achieved second-highest score on sentence-level propaganda classification.
Paper tackles class-imbalanced semi-supervised learning, improving performance.
problem Class imbalance in semi-supervised learning datasets.
method Proposes Suppressed Consistency Loss (SCL) to handle class imbalance.
result Method shows better performance in class-imbalanced semi-supervised learning environments.
CRCEN neural network tackles imbalanced classification.
problem Challenges in training conventional classifiers on imbalanced datasets.
method CRCEN neural network with a novel weighted cross entropy loss function.
result CRCEN outperforms baseline models on benchmark datasets.
Study challenges the necessity of data augmentation for improving predictions on imbalanced text datasets.
problem Improving predictions on imbalanced text datasets.
method Comparing classifier cutoff adjustments to data augmentation techniques.
result Classifier cutoff adjustments can produce similar results to data augmentation without the need for additional data.
Study shows imbalanced labels can be beneficial but not always in class-imbalanced learning.
problem Challenges in class-imbalanced learning with heavy label bias.
method Systematic investigation of semi-supervised and self-supervised approaches to leverage imbalanced labels.
result Imbalanced labels are valuable in semi-supervised learning but not always in self-supervised learning.
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.
PET-TURTLE improves clustering accuracy for imbalanced data.
problem Imbalanced data causes clustering errors.
method Generalizes cost function and introduces sparse logits.
result PET-TURTLE enhances overall clustering accuracy for imbalanced data.
Imbalance-XGBoost combines XGBoost with weighted and focal losses for binary imbalanced classification.
problem Binary label-imbalanced classification tasks.
method Combines XGBoost with weighted and focal losses.
result Empirical evaluations show state-of-the-art performance on Parkinson's disease classification.
Adversarial approach has been widely used for data generation in the last few years. However, this approach has not been extensively utilized for classifier training. In this paper, we propose an adversarial framework for classifier training that can also handle imbalanced data. Indeed, a network is trained via an adve…
ART adapts class-wise resampling to improve imbalanced classification performance.
problem Class imbalance in classification tasks limits model performance.
method ART uses adaptive resampling based on class-wise performance metrics.
result ART consistently outperforms other methods on diverse benchmarks.
Deep Convolutional Neural Networks (DCNN) has shown excellent performance in a variety of machine learning tasks. This manuscript presents Deep Convolutional Neural Fields (DeepCNF), a combination of DCNN with Conditional Random Field (CRF), for sequence labeling with highly imbalanced label distribution. The widely-us…
The paper corrects bias in synthetic data for imbalanced learning.
problem Challenges in balancing false positive and negative rates in imbalanced data.
method Proposes a bias correction procedure to generate synthetic data for minority groups.
result Enhances prediction accuracy while avoiding overfitting.
OMTL uses ontology to learn from imbalanced EHR data.
problem Imbalanced and small usable data for phenotypes in EHRs.
method Ontology-driven multi-task learning framework.
result Improved learning performance on phenotypes.
New deep learning method handles rare and imbalanced events in time series.
problem Challenges in event detection in time series data, especially rare and imbalanced events.
method Supervised regression-based deep learning approach that handles various types of events.
result Superior performance across diverse domains, particularly for rare events and imbalanced datasets.
Adaptive regularization tackles heteroskedastic and imbalanced datasets in deep learning.
problem Heteroskedastic and imbalanced datasets challenge deep learning due to varying label uncertainty and long-tailed label distributions.
method Data-dependent adaptive regularization that applies stronger regularization to higher-uncertainty, lower-density regions.
result Significant improvement in noise-robust deep learning over other methods on benchmark tasks.
An adjusted NN algorithm reduces false negatives in imbalanced data.
problem Learning from imbalanced data, focusing on reducing false negatives.
method Introduces a reweighted distance scheme to modify Voronoi regions and decision boundaries.
result The method yields the best performance, especially when combined with sampling methods.
A new framework SIMBA improves graph classification performance on size-imbalanced datasets.
problem Size imbalance in graph classification leads to poor model performance.
method Energy-guided structural smoothing between head and tail graphs, re-weighting based on energy propagation.
result SIMBA outperforms existing methods in size-imbalanced graph classification tasks.
A new framework boosts classifier performance on imbalanced data.
problem Learning from highly imbalanced massive datasets.
method Self-paced ensemble with under-sampling to harmonize data hardness.
result Robust performance even under overlapping classes and skewed distribution.
New method boosts multiclass classification speed and accuracy.
problem Multiclass classification problem
method Factorized MultiClass Boosting (decomposing into regression tasks, solving with CART trees)
result Significantly faster and same quality as state-of-the-art solutions
A new method for imbalanced binary classification without resampling.
problem Imbalanced binary classification tasks where majority class under-representation leads to information loss.
method Layered learning approach with two stages: clustering and classification.
result The method outperforms state-of-the-art methods in 100 benchmark data sets.
Model for detecting rare labels in imbalanced crowdsourcing data.
problem Detecting rare labels in imbalanced crowdsourcing data.
method Generative aggregation model combining item difficulty and class-dependent annotator competence.
result Our model achieves the highest minority recall while maintaining competitive balanced accuracy.
New methods improve deep learning on imbalanced datasets.
problem Poor performance of deep learning on imbalanced datasets.
method Label-distribution-aware margin (LDAM) loss and a training schedule.
result Combination of methods achieves significant performance gains.
Study proposes a differentiable surrogate loss function for optimizing Fβ score in binary classification with imbalanced data.
problem Non-differentiability of Fβ score makes it unsuitable for optimization by gradient-based learning. method Investigated relationship between Fβ score and loss functions, proposed a differentiable surrogate loss function. result Gradient paths of the proposed surrogate Fβ loss function approximate the gradient paths of the Fβ score. Imbalanced data with a skewed class distribution are common in many real-world applications. Deep Belief Network (DBN) is a machine learning technique that is effective in classification tasks. However, conventional DBN does not work well for imbalanced data classification because it assumes equal costs for each class.…
New benchmark and COAL model tackle class-imbalanced domain adaptation.
problem Aligning feature and label distributions across domains with different label distributions.
method COAL model combining feature and label distribution alignment.
result COAL model outperforms recent domain adaptation methods.
AMI-Net+ tackles medical diagnosis from incomplete, imbalanced data.
problem Medical diagnosis from incomplete and imbalanced data.
method AMI-Net+ uses multi-instance neural network with embedding, multi-head attention, and gated attention-based pooling. It also employs focal loss and self-adaptive multi-instance pooling.
result AMI-Net+ outperforms state-of-the-art models on real-world medical datasets.
BSAC improves credit scoring models by leveraging autoencoders and addressing imbalanced datasets.
problem Imbalanced and heterogeneous credit scoring datasets.
method Bagging Supervised Autoencoder Classifier (BSAC) that uses autoencoders and undersampling.
result BSAC improves classification of loan applicants, demonstrating robustness and effectiveness.
stream-learn is a Python library for analyzing data streams with various drift types.
problem Analyzing drifting and imbalanced data streams.
method Synthetic data stream generator, evaluation methodologies, and imbalanced binary classification metrics.
result Efficient implementation of classifiers for data stream analysis.
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.
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.
Study finds optimal vocabulary size for neural machine translation.
problem Imbalanced class distribution in language data affects NMT performance.
method Casts NMT as a classification task, analyzes vocabulary sizes, and tests multiple languages.
result Certain vocabulary sizes outperform others, explaining NMT performance.
The paper studies a rebalanced dataset for imbalanced classification using Centered Random Forests.
problem Imbalanced classification where one class is underrepresented.
method Theoretical analysis of Centered Random Forests (CRF) with rebalanced datasets and debiasing techniques.
result Theoretical Central Limit Theorem (CLT) for the infinite CRF and debiased estimator IS-ICRF.
Paper introduces a new loss function for deep imbalanced classification.
problem Class ambiguity and imbalance in large datasets.
method Stochastic top-K hinge loss based on smoothed top-K operator.
result Our loss function significantly outperforms other baseline loss functions in imbalanced datasets.
Diffusion models generate new samples by adding and removing noise.
problem Generating new samples from data.
method Apply noise to data, reverse the process to generate new samples.
result Diffusion models can improve classifier performance on imbalanced data.
Paper improves fraud detection in imbalanced financial data.
problem Detecting fraud in imbalanced financial datasets.
method Uses time-varying Poisson processes for fraud prediction.
result Method outperforms baseline in imbalanced data.
A new activation function improves credit scoring accuracy for imbalanced datasets.
problem Imbalanced datasets in credit scoring lead to underestimation of misclassification costs.
method Introduces ASIG, an asymmetric adjusted Sigmoid function.
result ASIG-embedded classifier outperforms traditional classifiers across various imbalance ratios.
Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions. While different strategies exist to tackle this problem, methods which generate artificial data to achieve a balanced cla…
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.
Learning from many real-world datasets is limited by a problem called the class imbalance problem. A dataset is imbalanced when one class (the majority class) has significantly more samples than the other class (the minority class). Such datasets cause typical machine learning algorithms to perform poorly on the classi…
DM improves deep model robustness for noisy, imbalanced datasets.
problem Noisy labels and imbalanced datasets in real-world large-scale datasets.
method Derivative Manipulation (DM) approach to example weighting.
result DM enhances robustness of deep models under adverse conditions.
VIR model improves regression accuracy and uncertainty estimation for imbalanced data.
problem Imbalanced regression datasets lead to poor model accuracy and uncertainty estimation.
method VIR model uses probabilistic smoothing and reweighting to estimate latent representations and uncertainty.
result VIR model outperforms state-of-the-art models in accuracy and uncertainty estimation.