The paper introduces CBE, a complexity‑adaptive boundary‑aware ensemble method designed to handle class imbalance in classification tasks. It links sampling weights to data complexity, dynamically adjusting boundary preference during under‑sampling, and employs a multi‑level complexity measure to categorize datasets into Easy, Median, and Hard. Experiments on 24 UCI and KEEL datasets show that CBE improves F‑measure and G‑mean on Easy and Median cases without harming performance on Hard cases.
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.
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.
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...
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.
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.
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.
arXiv:2607.22258v2 Announce Type: replace Abstract: Models trained on long-tailed data using standard softmax tend to exhibit higher training error and a larger generalisation gap for classes with fe...
arXiv:2607. 13555v1 Announce Type: cross Abstract: Bioacoustic call-type classification relies on costly expert annotation.
The paper introduces a collaborative optimization Boosting model for multiclass imbalanced learning that integrates density and confidence factors to create a noise‑resistant weight update mechanism and a dynamic sampling strategy. The modules are tightly coupled to coordinate weight updates, sample region partitioning, and region‑guided sampling. Experiments on 40 public imbalanced datasets show the model significantly outperforms seven state‑of‑the‑art baselines.
arXiv:2607. 22258v1 Announce Type: new Abstract: Deep learning models using traditional softmax classifiers have achieved remarkable success in various classification tasks.
arXiv:2505. 13518v3 Announce Type: replace-cross Abstract: Imbalanced datasets, where one class significantly outnumbers others, remain a persistent challenge in machine learning, often biasing predictions toward the majority class and degrading classifier performance.
TIGER is a time‑series classification method that uses a small set of three generic classifiers applied to four different representation families, producing twelve base learners. The predictions are stacked into a meta‑feature matrix and an adaptive meta‑classifier—choosing between a weighted hard majority vote and the pretrained TabICLv2 model—selects the best rule per dataset based on training sample size. On a 142‑dataset UCR benchmark, TIGER achieves the highest mean accuracy, balanced accuracy, and F1‑score among six compared algorithms, outperforming each constituent method and demonstrating strong generalization with a single hyperparameter.