arXiv:2409. 13007v3 Announce Type: replace-cross Abstract: Class imbalance poses a significant challenge in classification tasks, often causing standard learning algorithms to become biased toward the majority class.
By Asif Newaz, Asif Ur Rahman Adib, Taskeed Jabid
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
arXiv:2610.27443v1 Announce Type: new
Abstract: Set-valued classification (SVC) offers a practical approach to communicating uncertainty while maintaining accuracy by predicting variable-sized sets p...
By Kevin Kasa, Scott C. Lowe, Graham W. Taylor
arXiv:2606. 11761v1 Announce Type: new Abstract: Dynamic data pruning techniques aim to reduce computational cost while minimizing information loss by periodically selecting representative subsets of input data during model training.
By Atif Hassan, Swanand Khare, Jiaul H. Paik
The paper surveys tabular imbalanced learning and introduces TILBench, a benchmark evaluating over 40 methods on 57 datasets. It presents a unified taxonomy of approaches and shows that no single method dominates across all settings, with performance depending on dataset regimes and computational constraints. Practical recommendations for method selection and future research directions are provided.
By Ruizhe Liu, Jiaqi Luo
The paper argues that for fine‑tuning large language models, selecting training data based on coverage of the input space is more effective than selecting based on difficulty. While difficulty‑based scores work for smaller classification models, they often underperform random sampling for LLMs. The authors propose a simple clustering‑based method that improves coverage and achieves performance equal to or better than random selection, especially on multi‑domain tasks.
By Lalchand Pandia, Kanishka Misra, Allyson Ettinger