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.
By A. Quadir, A. Rahaman, P. N. Suganthan, M. Tanveer
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.
By Ziqiang Li, Yun Liu, Gouhei Tanaka
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.
By Shuyin Xia, Guoyin Wang, Xinbo Gao, Xiaoyu Lian, Hongzhi Kuai
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...
By Xiaoyu Lian, Yuchao Zhang, Shuyin Xia, Siqi Zhong, Xuzhao Xiang
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...
By Suzhen Yuan, Dehang Chen, Lifeng Shen, Shuyin Xia, Jeremiah D. Deng
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.
By Zeqiang Xian, Caihui Liu, Yong Zhang, Wenjing Qiu, Duoqian Miao, Witold Pedrycz
arXiv:2211. 11278v3 Announce Type: replace-cross Abstract: Nearest neighbour classification is attractive for tabular data, but its performance can deteriorate when a fixed query centred neighbourhood does not follow the local class geometry.
By Zardad Khan, Amjad Ali, Najd Adeed, Saeed Aldahmani
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.
By Zeqiang Xian, Caihui Liu, Yong Zhang, Wenjing Qiu
Curvature-Aware Radius Shrinkage for Adaptive Nearest Neighbor Classification (CARSANN) is a geometry-driven framework that adapts the spatial support of each neighborhood based on local geometric complexity. It estimates intrinsic dimensionality with TwoNN, builds an intrinsic representation via PCA, and uses a shape-operator-based estimate of local mean curvature to shrink the radius in highly curved regions while keeping a broader support in flatter areas. Experiments on over 70 OpenML datasets show that CARSANN consistently outperforms standard k‑NN and rivals other adaptive nearest‑neighbor methods, achieving a mean balanced accuracy increase from 0.6506 to 0.7528 and statistically significant improvements on most datasets.
By Alexandre L. M. Levada
arXiv:2601.01405v3 Announce Type: replace-cross
Abstract: Ball Mapper summarizes a finite metric dataset by covering the sample with closed balls centered at selected landmarks and connecting landmar...
By Jay-Anne Bulauan, John Rick Manzanares
arXiv:2607. 29115v1 Announce Type: cross Abstract: Link prediction aims to identify potential or future connections within a given graph structure.
By Sen Zhao, Cheng Liu, Shuyin Xia, Zhiyuan Liu, Yi Liu, Yi Wang, Wei Wang
arXiv:2606. 12077v1 Announce Type: new Abstract: Time-series clustering remains challenging due to the inherent trade-off between clustering effectiveness and computational efficiency.
By Yifan Wang, Lifeng Shen, Shuyin Xia, Yi Wang