arXiv AI
Jul 21
DADIR: Density-Aware Data-level Imbalanced Regression Framework
arXiv:2607. 17178v1 Announce Type: cross Abstract: Imbalanced learning addresses predictive modeling problems with underrepresented regions of the data distribution.
Hybrid Imbalanced Regression Through Unified Data-Level and Algorithm-Level Balancing
arXiv:2606. 01221v1 Announce Type: cross Abstract: Imbalanced learning is a critical challenge in machine learning, where underrepresented target values can bias models and degrade prediction performance on rare but important cases.
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arXiv AI
Jun 16
Beyond Rebalancing: Benchmarking Binary Classifiers Under Class Imbalance Without Rebalancing Techniques
arXiv:2509. 07605v2 Announce Type: replace-cross Abstract: Class imbalance poses a significant challenge to supervised classification, particularly in critical domains like medical diagnostics and anomaly detection where minority class instances are rare.
arXiv AI
Jul 21
Data Balancing Strategies: A Systematic Survey of Resampling and Augmentation Methods
arXiv:2505. 13518v3 Announce Type: replace-cross Abstract: Imbalanced datasets, where one class significantly outnumbers others, remain a persistent challenge in machine learning, often biasing predictions toward the majority class and degrading classifier performance.
arXiv Machine Learning
Jun 25
Model-agnostic Mitigation Strategies of Data Imbalance for Regression
arXiv:2506. 01486v2 Announce Type: replace Abstract: Data imbalance persists as a pervasive challenge in regression tasks, introducing bias in model performance and undermining predictive reliability.
arXiv AI
Jun 9
Large Language Models for Imbalanced Classification: Diversity makes the difference
arXiv:2510. 09783v2 Announce Type: replace-cross Abstract: Oversampling is one of the most widely used approaches for addressing imbalanced classification.
arXiv AI
Jun 29
iCost: A Novel Instance-Complexity-Based Cost-Sensitive Learning Framework
arXiv:2409. 13007v3 Announce Type: replace-cross Abstract: Class imbalance poses a significant challenge in classification tasks, often causing standard learning algorithms to become biased toward the majority class.
arXiv AI
Jun 10
Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning
arXiv:2606. 10250v1 Announce Type: cross Abstract: Class imbalance is a common problem in deep learning that severely degrades performance.
arXiv Machine Learning
Aug 4
Learning the Pareto Frontier of Predictive Models under Distribution Shift
arXiv:2608. 00632v1 Announce Type: new Abstract: Modern machine learning pipelines increasingly rely on reusing pretrained and foundation models across downstream tasks.
arXiv AI
Jun 9
Customer Churn Prediction on Structured Data Using FT-Transformer and Stacking Ensembles
arXiv:2606. 07582v1 Announce Type: cross Abstract: Customer churn prediction is essential across data-driven industries such as insurance, digital banking, eCommerce, and subscription platforms, where retaining existing customers is typically more cost-effective than acquiring new ones.
arXiv Machine Learning
Aug 10
Certified Interpolation Oversampling: Per-Instance Safety Guarantees for Imbalanced Learning
arXiv:2501. 15790v2 Announce Type: replace Abstract: Synthetic minority oversampling is typically designed and evaluated against a predictive objective, generating samples that improve downstream classification.
arXiv Machine Learning
Jul 7
WeightCLIP: Aligning Datasets and Models for Weight Space Learning
arXiv:2607. 03551v1 Announce Type: new Abstract: Weight space learning aims to learn representations of neural network (NN) weights, enabling different downstream tasks.
arXiv AI
Aug 12
BPG: Balancing Plasticity and Generalization for Domain Incremental Learning
arXiv:2608. 10804v1 Announce Type: cross Abstract: Deep neural networks excel in various tasks but struggle to generalize across evolving data distributions, leading to significant performance degradation under domain shifts.