Aug 2026· International Journal of Data Science and Analysis· Vol 22· 0 citations· 35 references
TL;DR
This work proposes a novel hybrid approach that combines the strengths of the extreme learning machine (ELM) and the twin support vector machine (TSVM) to address the challenges of robustness and scalability in multi-label classification, particularly in settings where deep learning is not practical due to limited training instances.
This review consolidates the landscape of CP adaptations for MLL under a unified framework, examining the types of outputs and guarantees they provide, where label dependencies are incorporated, and how inference cost scales with the number of labels.
LFS-FRAME is proposed, a Leakage-Free Stacked ensemble framework that integrates functional learning using Kolmogorov-Arnold Networks (KAN) and rule-based learning via XGBoost and rule-based learning via XGBoost for robust multiclass classification.
S. P. Sharmila, Aruna Tiwari· arXiv.org· 0 citations
A systematic cross-domain study of KLPCDA reveals consistent patterns in the interaction of the three core objectives variance, between-class, and within-class terms, providing a unified and interpretable understanding of their roles in stabilizing representations and enhancing discrimination in SSS settings.
Multi-label classification (MLC) seeks to make multiple predictions for an instance by identifying associations between labels, thereby enhancing prediction capability. The paper presents a new method for combining improved chicken swarm optimization (ICSO) feature selection with recurrent neural networks (RNNs) for ML...
M. Priyadharshini, Narendruni Lakshmi Priya, Anitha Vippdapu et al.· Bulletin of Electrical Engin...· 0 citations
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
SafeVAE-GAN is proposed, a resampling framework that integrates a conditional β-Variational Autoencoder with Wasserstein GAN to generate high-quality minority samples and provides a robust and scalable solution for multi-class imbalanced learning across diverse domains.
Nhiem Ba Nguyen, Sinh Van Nguyen, B. Nguyễn· Vietnam Journal of Computer...· 0 citations
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