arXiv Machine Learning August 19, 2026 By A. Rahaman, A. Quadir, M. Tanveer

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

Read the original on arXiv Machine Learning →

arXiv:2608. 16965v1 Announce Type: new Abstract: The dominance of majority classes in real-world datasets poses a fundamental challenge to randomized neural networks, often biasing decision boundaries and overlooking critical minority samples.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

benchmarks

arXiv Machine Learning

2d ago

Robust Dual-Model Collaborative Random Vector Functional Link Network

arXiv:2608. 13628v1 Announce Type: new Abstract: 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.

By A. Quadir, A. Rahaman, Mushir Akhtar, M. Tanveer

benchmarks

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.

By Jelke Wibbeke, Sebastian Rohjans, Andreas Rauh

benchmarks safety

arXiv AI

1d ago

ER-KANs: Efficient and Robust Kolmogorov-Arnold Networks for Data-Scarce Scientific Machine Learning

arXiv:2608. 14773v1 Announce Type: cross Abstract: The efficient-KAN literature---covering Chebyshev, wavelet, and radial-basis-function variants of the original Kolmogorov-Arnold Network---has been benchmarked almost entirely on clean data.

By Harshil Lodhiya

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.

By Behnam Yousefimehr, Mehdi Ghatee, Javad Fazli, Shervin Ghaffari, Zahra Rafei, Mohammad Amin Seifi, Sajed Tavakoli, Abolfazl Nikahd, Mahdi Razi Gandomani, Alireza Orouji, Ramtin Mahmoudi Kashani, Sarina Heshmati, Negin Sadat Mousavi

diffusion efficiency safety

arXiv AI

1d ago

DirMixE: Harnessing Test Agnostic Long-tail Recognition with Hierarchical Label Variations

arXiv:2405. 07780v3 Announce Type: replace-cross Abstract: This paper explores test-agnostic long-tail recognition, a challenging long-tail task where the test label distributions are unknown and arbitrarily imbalanced.

By Zhiyong Yang, Qianqian Xu, Sicong Li, Zitai Wang, Xiaochun Cao, Qingming Huang

fine-tuning

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.

By Haengbok Chung, Jae Sung Lee

benchmarks safety

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