Uncertainty-Aware Ensemble Deep Randomized Neural Networks for Classification

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

RoBell-RVFL is proposed, a robust and lightweight generalized bell random vector functional link network that redefines how randomized models handle class imbalance and noisy data and achieves adaptive control over sample contributions without sacrificing the closed-form learning efficiency of RVFL networks.

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.

Embedded One-Class Classification for Deep Neural Network Representations

An Embedded One-Class Classification (EOCC) framework for monitoring task-informed neural network representations andComparisons with depth-based, density-based, covariance-based, isolation-based, and end-to-end deep one-class methods show that EOCC is competitive and frequently achieves low Type II error while maintaining the nominal in-control acceptance probability.

MC-SNN: Multicenter Stochastic Neural Network for Adversarially Robust Learning.

A multicenter learning method that leverages the advantage of stochastic neural networks (SNNs) for feature uncertainty learning and induces multiple centers for each class of samples in latent space to fit data more delicately, named the multicenter SNN (MC-SNN).

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

Weighted Deep Embedded Clustering for Robust Representation Learning in Noisy Data

Deep clustering methods effectively learn meaningful representations from unlabeled data. Typically, existing approaches treat all samples equally during training, which can lead to unstable clustering in the presence of noise and outliers. In this research, we propose a Reliability-Based Deep Embedded Clustering (RDEC) approach that improves clustering reliability through a novel distance-aware weighting strategy. Specifically, rather than assigning equal importance to all samples, RDEC estimates and exploits the typicality of each instance according to its distance from the cluster centers in the latent space. Accordingly, samples that lie closer to cluster centers are assigned higher weights, while distant, atypical and ambiguous samples are gradually down-weighted using an inverse polynomial function. Moreover, the proposed weighting approach is coupled with a novel Kullback–Leibler divergence objective function to focus on the most representative data instances and guide the clustering process. Furthermore, the resulting latent distances and reliability weights demonstrate that RDEC effectively distinguishes reliable samples from noisy ones, resulting in more stable clustering behavior. RDEC was evaluated on three benchmark image datasets, namely MNIST, USPS, and Fashion-MNIST, under both clean conditions and controlled image corruption settings, including semantic outliers and synthetic perturbations, to assess its robustness during clustering. The experimental results demonstrated that RDEC achieved competitive clustering performance compared with representative clustering methods evaluated under the same experimental setting. In particular, RDEC yielded the best overall performance on the clean USPS dataset, attaining an ACC, NMI, and ARI of 0.8079, 0.7714, and 0.7392, respectively. Moreover, on the clean Fashion-MNIST dataset, RDEC achieved the highest NMI and ARI while maintaining competitive clustering accuracy, demonstrating its effectiveness on a more challenging clustering benchmark. Under these controlled image corruption scenarios, RDEC consistently achieved strong clustering performance across multiple corruption levels on MNIST, USPS, and Fashion-MNIST datasets, demonstrating the effectiveness of the proposed reliability-aware weighting mechanism under diverse image characteristics. The present study focuses on robustness under controlled image-based corruption scenarios, with the evaluation limited to benchmark image datasets.