arXiv:2607. 22258v1 Announce Type: new Abstract: Deep learning models using traditional softmax classifiers have achieved remarkable success in various classification tasks.
By Yi-Hang Zhu, Rajeev Raman, Shiqi Su, Jianyuan Sun, Xinyu Yang, Nan Xing, Huiyu Zhou
arXiv:2606. 27997v1 Announce Type: new Abstract: Benchmarks of machine learning models often include many datasets, making evaluation expensive.
By Rostislav Gusev, Alexey Zaytsev
arXiv:2606. 31686v1 Announce Type: cross Abstract: Feature rankings are widely used in supervised feature selection because they are simple, scalable and easy to interpret.
By Jesus S. Aguilar-Ruiz
arXiv:2606. 26053v1 Announce Type: cross Abstract: Synthetic data augmentation is widely used to mitigate class imbalance, but its theoretical effects on score-based classification remain poorly understood.
By Zhengchi Ma, Pengfei Lyu, Anru R. Zhang
Synthetic data augmentation is widely used to mitigate class imbalance, but its theoretical effects on score-based classification remain poorly understood. This paper develops a framework for characterizing when synthetic minority augmentation can improve threshold-integrated and threshold-optimized metrics, including AUROC, AUPRC, best-threshold balanced accuracy, and best-threshold \(\F_1\) score.
arXiv:2509. 07605v2 Announce Type: replace-cross Abstract: Class imbalance poses a significant challenge to supervised classification, particularly in critical domains like medical diagnostics and anomaly detection where minority class instances are rare.
By Ali Nawaz, Amir Ahmad, Shehroz S. Khan