Jul 2026· International Journal of Machine Learning and Cybernetics· Vol 17· 1 citation· 41 references
Computer Science
TL;DR
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.
A novel oversampling algorithm: the adaptive weighting–synthetic minority oversampling technique (AW-SMOTE), which combines the two perspectives of boundary tightness and local density and provides global sample enhancement support.
Imbalanced data classification is one of the difficult machine learning tasks in which the majority class outnumbers the minority classes. This problem is prevalent in various domains such as medical disease detection, spam/fraud detection, digital marketing, agriculture and telecommunications. Synthetic Minority Over-...
Ruturaj Mahajan, S. Patil, Vilabha Patil· WSEAS Transactions on Comput...· 0 citations
SMOG, an adaptive hybrid oversampling framework that integrates the Synthetic Minority Over-sampling Technique with a Conditional Generative Adversarial Network (GAN)-based difficulty-aware learning strategy, highlights the effectiveness of adaptive hybrid generative strategies for intelligent learning on imbalanced da...
Jatinder Kaur, Vimal Parmar, B. K. Rao et al.· Evolutionary Intelligence· 0 citations
Class imbalance remains a critical challenge in supervised learning, often biasing classifiers toward majority classes. While resampling techniques like Synthetic Minority Oversampling Technique (SMOTE) are widely used, the combined effect of data balancing and hyperparameter optimization across diverse datasets is rar...
Necati Vardar, Mehmet Fatih Ören· Sakarya University Journal o...· 0 citations
This thesis focus on enhancing the performance of XGBoost for the classification of imbalanced data by performing k-fold cross validation and hyper- parameters tuning.
Amit Rauniyar, Prem Chandra Roy, Anisha Pokhrel et al.· Journal of Hillside College...· 0 citations
Class imbalance is a critical challenge in the classification of tabular data, since it affects the diagnostic capacity of models in domains such as health and finance. This research compares four synthetic data generation paradigms: traditional interpolation (SMOTE-NC), deep generative models (CTGAN and TVAE), and a h...
Jhonatan Esquivel, Christian Humpiri, Jose M. Vega et al.· International Journal of Adv...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
