Robust SMOTE-enhanced intuitionistic fuzzy LSTSVM for imbalanced and noisy data classification

Bo Liu Jia-Jun Chen Yanshan Xiao Qing-Shan Tang Wei-Jie Zeng Xin-Zhe Jiang Yun-Long Sun Zong-Xiong Yang

Jul 2026 · International Journal of Machine Learning and Cybernetics · Vol 17 · 0 citations · 37 references

Computer Science

TL;DR

Improved robustness in noisy and imbalanced settings is indicated, and the reduced performance variance across repeated trials indicates improved robustness in noisy and imbalanced settings.

DOI Semantic Scholar

View source

Similar papers

Jul 2026

SVED-SMOTE: support vector enhanced density SMOTE oversampling technique for imbalanced data classification

The results demonstrate that SVED-SMOTE significantly outperforms state-of-the-art oversampling techniques across multiple metrics, particularly in datasets with complex distributions and severe class overlap.

Jiao Wang, N. Awang · International Journal of Mac... · 1 citation

Open access Aug 2026

ASWBoost: Classification algorithm for noisy and imbalanced data based on parametric exponential loss

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

Open access Sep 2026

Intuitionistic Fuzzy Least Square Projection Twin Support Vector Machine for Pattern Classification

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...

Xin Zhang, Xiaopeng Hua · Symmetry · 0 citations

Open access 2026

A Novel Entropy-Based Framework for Hybrid Sampling in Imbalanced Learning

Findings indicate that entropy-based hybrid sampling is a promising approach for structured tabular imbalanced classification, while further validation on high-dimensional text and image datasets remains necessary to establish broader generalizability.

Ren-Jieh Kuo, Muhammad Rizki, F. E. Zulvia et al. · Computers, Materials & C... · 0 citations

Preprint Aug 2026

Uncertainty-Aware Ensemble Deep Randomized Neural Networks for Classification

Extensive experiments conducted on UCI and KEEL benchmark datasets demonstrate the superiority of the proposed IF-dRVFL and IF-edRVFL models over existing SOTA fuzzy and non-fuzzy approaches.

M. Sajid, A. Quadir, A. Rahaman et al. · 0 citations

Open access Aug 2026

Fuzzy Random Forest: Integrating Fuzzy Set Theory for Enhanced Imbalanced Classification

Fuzzy Random Forest is particularly suited for applications requiring high precision in minority class identification and interpretable fuzzy decision rules, such as medical diagnosis, fraud detection, and credit risk assessment where regulatory compliance demands transparent decision-making.

James Omusula Atsali · Asian Journal of Probability... · 0 citations