arXiv AI
PU classification under Non-SCAR: clustering-assisted logistic model with oversampling enhancement
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arXiv Machine Learning
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Collaborative Optimization of Multiclass Imbalanced Learning: Density-Aware and Region-Guided Boosting
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 Machine Learning
Sep 22
Focused PU learning from imbalanced data
arXiv:2605.14467v2 Announce Type: replace Abstract: We propose a new method of learning from positive and unlabeled (PU) examples in highly imbalanced datasets. Many real-world problems, such as dise...
arXiv AI
Jun 16
Beyond Rebalancing: Benchmarking Binary Classifiers Under Class Imbalance Without Rebalancing Techniques
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 Machine Learning
Sep 23
Density-Ratio Rescoring for Imbalanced Classification Using Raking Duals and Classifier Scores
Density‑Ratio Rescoring (DRR) enhances a classifier trained with the original class prior by adding a survey‑raking dual score that reweights the majority class to match minority feature moments within a tolerance. The method standardizes both the dual and base scores, combines them with equal weight, and uses the fitted dual directly for prediction without resampling or refitting the base model. Experiments on 24 tabular benchmarks and a gene‑expression cohort show that DRR improves average precision over the standardized base on every dataset, with a mean gain of 0.034, and outperforms a shared‑dual raking‑and‑relabeling resampler on most datasets.
arXiv AI
2d ago
Partial AUC Maximization from Positive-unlabeled Data
arXiv:2610.00284v1 Announce Type: cross Abstract: The partial area under the receiver operating characteristic curve (pAUC) is an important performance metric for binary classification that summarize...
arXiv AI
Jun 29
iCost: A Novel Instance-Complexity-Based Cost-Sensitive Learning Framework
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.