arXiv:2608. 12903v1 Announce Type: new Abstract: The $k$-Nearest Neighbor~(KNN) algorithm is widely used across various tasks.
By Xiaoyu Lian, Shuyin Xia, Hongxuan He, Lifeng Shen, Guoyin Wang, Xinbo Gao
arXiv:2605.01418v2 Announce Type: replace
Abstract: Time-series data are inherently multiscale, spanning diverse temporal granularities from coarse trends to fine-scale dynamics. However, existing ti...
By Seokhyun Lee, Jaeho Kim, Changjun Oh, Mihaela van der Schaar, Changhee Lee
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: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
arXiv:2607. 07500v1 Announce Type: cross Abstract: Time series classification (TSC) is dominated by a two-stage paradigm: train a feature encoder -- either from scratch on the target dataset or via pretraining on large corpora -- and then fit a task-specific classifier on top.
By Jaris K\"uken, Shi Bin Hoo, Martin Mr\'az, Frank Hutter, Lennart Purucker
The paper introduces a Time-Aware Bag-of-Receptive-Fields (BORF) for classifying irregular time series, extending the original BORF to handle non-uniform sampling, missing data, and variable lengths. It adds a time-weighted normalization that weights observations by their time deltas, enabling pattern extraction that reflects the true temporal distribution. The method maintains linear time complexity and is evaluated against state‑of‑the‑art irregular time‑series classifiers, achieving competitive performance while providing human‑interpretable explanations.
By Francesco Spinnato