Jul 2026· International Journal of Data Science and Analysis· Vol 22· 0 citations· 43 references
Computer Science
TL;DR
A novel model, termed granular-ball intuitionistic fuzzy twin support vector machine with RVFL-based feature enhancement (RGBIFTSVM), which employs granular balls (GBs) to mitigate the influence of noise and outliers, and utilizes a random vector functional link (RVFL) network to enhance feature representation in a nonlinear manner.
The Least Square Projection Twin Support Vector Machine (LSPTSVM) is an effective machine learning tool for solving classification problems. However, LSPTSVM does not account for the contribution of each sample during training, making it susceptible to outliers and noise. This susceptibility diminishes its generalizati...
A fuzzy granular-ball random vector functional link (GBFRVFL) framework that leverages granular-ball computing to abstract raw samples into adaptive granular balls and introduces two membership assignment schemes that enhance robustness to noise, outliers, class imbalance, and uncertainty in granular-ball distributions...
A. Quadir, A. Rahaman, P. Suganthan et al.· 0 citations
In this presentation, we introduce a distributionally robust Twin Support Vector Machine (TSVM) model designed to tackle bi-class and multi-class classification challenges under uncertainty. Our model leverages first and second-order information to enhance robustness. By solving two interconnected chance-constrained SV...
We introduce a robust classification model designed for feature selection. Support vector machine (SVM) models continue to play a crucial role in binary classification, particularly with tabular data. Their robust variants are essential for developing classifiers that remain stable despite shifts in data distribution...
Miguel Carrasco, B. Ivorra, Julio López et al.· Journal of Convex Analysis· 0 citations
Hierarchical Takagi–Sugeno–Kang (TSK) fuzzy classifiers combine nonlinear modeling with rule-based decision structures for complex classification tasks. However, prediction errors may propagate through successive layers, and the contributions of different layers require careful consideration when combining their output...
Yue Xin, Wen-Liang Li, Ta Zhou· Symmetry· 0 citations
ASWBoost, a robust boosting algorithm, is proposed by introducing a parameter θ to modify the exponential loss, and significantly outperforms AdaBoost, CS-AdaBoost, GBDT, SMOTEBoost, and RUSBoost, with overall performance comparable to XGBoost.
Fei Meng, Mei Yan, Hang Liu et al.· PLoS ONE· 0 citations
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