RoBell-RVFL: A Robust Generalized Bell Random Vector Functional Link Network

Uncertainty-Aware Ensemble Deep Randomized Neural Networks for Classification

Extensive experiments conducted on UCI and KEEL benchmark datasets demonstrate the superiority of the proposed IF-dRVFL and IF-edRVFL models over existing SOTA fuzzy and non-fuzzy approaches.

Robust Dual-Model Collaborative Random Vector Functional Link Network

Random vector functional link (RVFL) networks are lightweight and fast neural models that offer efficient training and strong generalization through randomized hidden-layer weights and direct input-output connections. However, conventional RVFL models are sensitive to noisy labels, outliers, and imbalanced data, which limits their performance in real-world applications. To address these challenges, we propose the kernel risk-sensitive mean p-power based RVFL (KRPRVFL) model, which integrates the computational efficiency of RVFL with the robustness of the kernel risk-sensitive mean p-power (KRP) criterion. By replacing the standard least-squares objective with a KRP-based loss, KRPRVFL adaptively reduces the influence of corrupted or unreliable samples during training, resulting in improved stability and generalization. Additionally, a collaborative learning mechanism is introduced to enable adaptive interaction among model components, further enhancing robustness in complex and noisy environments. The proposed framework also leverages kernel-induced feature mapping to capture nonlinear relationships without requiring explicit hidden-layer selection, maintaining both efficiency and scalability. Extensive experiments on UCI and KEEL benchmark datasets demonstrate that KRPRVFL consistently outperforms baseline models in terms of accuracy, robustness, and statistical significance, highlighting its effectiveness as a fast, scalable, and reliable solution for challenging classification tasks.

When Data Imbalance Helps: Robust Generalization Through Shortcut Saturation

Through mechanistic analysis, a mechanistic pathway consistent with imbalance promoting generalization is characterized: a mechanistic pathway consistent with imbalance promoting generalization in sufficiently capable models.

RUBRIC: Realism--Utility Balanced Ranking for Imbalanced Classification

Class imbalance poses a fundamental challenge in risk-sensitive applications such as fraud detection and medical diagnosis, where minority-class samples are scarce yet critical for accurate classification. Existing oversampling methods generate synthetic samples to rebalance class distributions; however, they often produce large numbers of low-quality candidates that distort decision boundaries or introduce artifacts, leading to overfitting and degraded generalization. In this work, we introduce RUBRIC, a generator-agnostic filtering framework that formulates synthetic sample selection as a quality-over-quantity optimization problem. RUBRIC ranks candidates using a realism-utility trade-off: realism is quantified by a learned discriminator that distinguishes real samples from synthetic samples, while utility captures proximity to the decision boundary through a concave margin-based scoring function. We show that, under mild regularity conditions, the proposed filtering strategy monotonically tightens the generalization bound for margin-based classifiers by jointly reducing distribution shift and suppressing near-negative tail contributions. Through extensive experiments on credit-card fraud detection and other imbalanced benchmarks, we demonstrate that RUBRIC improves F1-macro and recall while maintaining comparable ROC-AUC across several generators. We also provide explicit lambda-sensitivity analysis to show how users can recover AUPRC when ranking quality is prioritized.

Robust SMOTE-enhanced intuitionistic fuzzy LSTSVM for imbalanced and noisy data classification

NADOS: Reliability-Gated Difficulty-Aware Oversampling for Noisy Imbalanced Classification

Class imbalance becomes more challenging when minority underrepresentation is accompanied by label noise, because synthetic oversampling may amplify unreliable local structures if noisy minority samples are used as interpolation seeds. This study proposes Noise-Aware Adaptive-Difficulty Oversampling (NADOS), which separates the assessment of seed trustworthiness from the allocation of synthesis effort. NADOS evaluates each minority instance through two local criteria. A reliability score determines whether the instance is suitable for synthetic generation, while a difficulty score assigns synthesis priority among eligible instances. The method is evaluated on 22 binary imbalanced benchmark datasets, five controlled label-noise levels, four classifier families, and nine oversampling methods. Across the full benchmark, NADOS obtains average F1 = 0.7428, G-mean = 0.8511, balanced accuracy = 0.8552, and AUPRC = 0.7700. These results give NADOS the strongest average F1, G-mean, and balanced accuracy, while remaining competitive on AUPRC. Non-parametric statistical tests show significant differences among the compared methods and indicate that NADOS is consistently competitive under noisy imbalanced learning conditions. Separating seed reliability from synthesis difficulty provides a practical strategy for noisy imbalanced classification. The reliability gate limits unsafe seed usage, while the difficulty score preserves attention to difficult but trustworthy minority samples.