The paper introduces GBFRVFL, a fuzzy granular-ball random vector functional link network designed to improve robustness in noisy, imbalanced, or uncertain data settings. It employs granular-ball computing to group raw samples into adaptive balls and proposes two membership assignment schemes: F-GBRVFL, which uses fuzzy membership to gauge ball reliability, and SDAP-GBRVFL, which introduces a statistical density‑adaptive Pythagorean membership that adjusts based on class variance, local sparsity, and ball compactness. Experiments on 37 UCI and KEEL datasets show that these models outperform baseline methods in both clean and noisy conditions, achieving higher accuracy and stability.
arXiv:2304. 11171v5 Announce Type: replace-cross Abstract: To overcome the limitations of point-based inputs, overly fine computation and limited adaptability in existing artificial intelligence methods, Guoyin Wang and Shuyin Xia proposed granular-ball computing as a new artificial intelligence learning paradigm.
arXiv:2608. 12903v1 Announce Type: new Abstract: The $k$-Nearest Neighbor~(KNN) algorithm is widely used across various tasks.
arXiv:2605. 08759v3 Announce Type: replace Abstract: Existing granular-ball generation methods are still mainly driven by handcrafted quality measures and heuristic splitting or stopping criteria, which may weaken the transparency of local generation decisions in clustering.
arXiv:2608. 11704v1 Announce Type: cross Abstract: Dynamic Time Warping (DTW)-based Nearest-Neighbor (NN) classifiers are effective for time-series classification but are vulnerable to mislabeled training samples and require numerous DTW computations during inference.
arXiv:2608. 10007v1 Announce Type: cross Abstract: The current state-of-the-art (SOTA) deep randomized neural networks, such as deep Random Vector Functional Link (dRVFL) and ensemble deep RVFL (edRVFL), treat all training samples uniformly, which limits their robustness and effectiveness when applied to real-world datasets containing noise and outliers.
RoBell-RVFL is a lightweight, quality‑aware generalized bell random vector functional link network designed to address class imbalance and noisy data in real‑world datasets. It uses a dual‑strategy sample‑level weighting: unit weights preserve minority class information, while a probability‑weighted generalized bell membership function suppresses noisy majority samples in a kernel‑induced feature space. Experiments on UCI and KEEL benchmarks, including tests with up to 40% label noise, show that RoBell‑RVFL consistently outperforms recent RVFL variants, demonstrating the importance of adaptive, quality‑aware sample weighting for robust learning.
arXiv:2609.05952v1 Announce Type: new Abstract: Nearest-neighbor classification is widely used in machine learning, yet existing methods often suffer from low computational efficiency and limited rob...
arXiv:2609. 20194v1 Announce Type: cross Abstract: Triangular membership functions (MFs) are widely used in fuzzy systems because of their interpretability, low parameterization complexity, and strong locality properties.
arXiv:2609.00647v1 Announce Type: new Abstract: Multiple kernel $k$-means integrates complementary nonlinear similarities by learning a combination of base kernels. Its pointwise optimization, howeve...
arXiv:2605. 22410v2 Announce Type: replace Abstract: Spectral clustering largely depends on the affinity graph, yet constructing a graph that preserves reliable local connectivity while adapting to heterogeneous data structures remains challenging.
arXiv:2606. 12077v1 Announce Type: new Abstract: Time-series clustering remains challenging due to the inherent trade-off between clustering effectiveness and computational efficiency.
arXiv:2607. 29115v1 Announce Type: cross Abstract: Link prediction aims to identify potential or future connections within a given graph structure.