QC-SMOTE: Quality-Controlled SMOTE for Imbalanced Classification

• URL: http://arxiv.org/abs/2606.24625v1
• Date: Tue, 23 Jun 2026 14:23:18 GMT
• Title: QC-SMOTE: Quality-Controlled SMOTE for Imbalanced Classification
• Abstract summary: QC-SMOTE estimates minority sample reliability using a composite neighbourhood trustworthiness score.<n>Synthetic candidates are generated using an IPQ-guided best-of-K strategy.
• Score: 11.542407696902115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Class imbalance poses a significant challenge in classification, where existing methods such as SMOTE often generate low-quality synthetic samples in regions with noise or class overlap. We propose QC-SMOTE, a quality-controlled oversampling framework that estimates minority sample reliability using a composite neighbourhood trustworthiness score combining local density, safe-level, and isolation from the majority class. Synthetic candidates are generated using an IPQ-guided best-of-K strategy that evaluates midpoint purity and, when required, majority clearance, with allocation guided by sample reliability and boundary informativeness. Generation behaviour adapts across overlap--imbalance regimes, adjusting interpolation range and selection criteria to match local data geometry. Low-quality synthetic samples are replaced with original minority duplicates when neighbourhood purity falls below an adaptive threshold, providing graceful degradation by reverting to duplication in severely noisy regions. Experiments on 30 imbalanced datasets using repeated stratified cross-validation show that QC-SMOTE achieves the strongest average AUC-ROC and Macro F1 among the compared oversampling methods, with particularly clear gains under moderate and severe imbalance. These results demonstrate the importance of quality-aware, geometry-adaptive synthetic sampling for robust imbalanced classification.

Related papers

• RUBRIC: Realism--Utility Balanced Ranking for Imbalanced Classification [38.8278639079174]
  Class imbalance poses a fundamental challenge in risk-sensitive applications such as fraud detection and medical diagnosis.<n>We introduce RUBRIC, a generator-agnostic filtering framework that formulates synthetic sample selection as a quality-over-quantity optimization problem.<n>We show that RUBRIC improves F1-macro and recall while maintaining comparable ROC-AUC across several generators.
  arXiv  Detail & Related papers  (2026-07-10T06:37:12Z)
• Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detection [2.063079740825497]
  Credit card fraud detection is fundamentally challenged by extreme class imbalance.<n>This paper introduces Q-SYNTH, a hybrid classical--quantum generative adversarial framework.
  arXiv  Detail & Related papers  (2026-05-20T13:32:18Z)
• Multimodal Learning on Low-Quality Data with Conformal Predictive Self-Calibration [72.0672328514289]
  Multimodal learning often grapples with the challenge of low-quality data.<n>We propose a unified framework, termed Conformal Predictive Self-Calibration.<n>Our framework consistently outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-05-05T14:48:52Z)
• Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data [55.84428098924793]
  We propose Constrained Uniform Top-K Sampling (CUTS), a parameter-free decoding strategy enforcing structure-preserving exploration.<n>We integrate this into Mixed-CUTS, a training framework synergizing exploitative and exploratory rollouts to amplify intra-group advantage variance.<n> Notably, Mixed-CUTS improves Pass@1 accuracy on the challenging AIME25 benchmark by up to 15.1% over standard GRPO.
  arXiv  Detail & Related papers  (2026-04-20T16:43:28Z)
• Test-time Adaptive Hierarchical Co-enhanced Denoising Network for Reliable Multimodal Classification [55.56234913868664]
  We propose Test-time Adaptive Hierarchical Co-enhanced Denoising Network (TAHCD) for reliable learning on multimodal data.<n>The proposed method achieves superior classification performance, robustness, and generalization compared with state-of-the-art reliable multimodal learning approaches.
  arXiv  Detail & Related papers  (2026-01-12T03:14:12Z)
• Towards Syn-to-Real IQA: A Novel Perspective on Reshaping Synthetic Data Distributions [74.00222571094437]
  Blind Image Quality Assessment (BIQA) has advanced significantly through deep learning, but the scarcity of large-scale labeled datasets remains a challenge.<n>We make a key observation that representations learned from synthetic datasets often exhibit a discrete and clustered pattern that hinders regression performance.<n>We introduce a novel framework SynDR-IQA, which reshapes synthetic data distribution to enhance BIQA generalization.
  arXiv  Detail & Related papers  (2026-01-01T06:11:16Z)
• Rebalancing with Calibrated Sub-classes (RCS): A Statistical Fusion-based Framework for Robust Imbalanced Classification across Modalities [16.993547305381327]
  Rebalancing with Calibrated Sub-classes (RCS) is a novel distribution calibration framework for robust imbalanced classification.<n>RCS fuses statistical information from the majority and intermediate class distributions via a weighted mixture of Gaussian components.
  arXiv  Detail & Related papers  (2025-10-10T00:06:13Z)
• Adaptive kernel-density approach for imbalanced binary classification [2.3126620160776845]
  Class imbalance is a common challenge in real-world binary classification tasks.<n>We propose a novel approach called Kernel-density-Oriented Threshold Adjustment with Regional Optimization (KOTARO)<n>In KOTARO, the bandwidth of Gaussian basis functions is dynamically tuned based on the estimated density around each sample.
  arXiv  Detail & Related papers  (2025-10-05T05:45:00Z)
• Learning Majority-to-Minority Transformations with MMD and Triplet Loss for Imbalanced Classification [0.5390869741300152]
  Class imbalance in supervised classification often degrades model performance by biasing predictions toward the majority class.<n>We introduce an oversampling framework that learns a parametric transformation to map majority samples into the minority distribution.<n>Our approach minimizes the mean maximum discrepancy (MMD) between transformed and true minority samples for global alignment.
  arXiv  Detail & Related papers  (2025-09-15T01:47:29Z)
• Generate Aligned Anomaly: Region-Guided Few-Shot Anomaly Image-Mask Pair Synthesis for Industrial Inspection [53.137651284042434]
  Anomaly inspection plays a vital role in industrial manufacturing, but the scarcity of anomaly samples limits the effectiveness of existing methods.<n>We propose Generate grained Anomaly (GAA), a region-guided, few-shot anomaly image-mask pair generation framework.<n>GAA generates realistic, diverse, and semantically aligned anomalies using only a small number of samples.
  arXiv  Detail & Related papers  (2025-07-13T12:56:59Z)
• Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection [82.43311784594384]
  Real-world datasets contain not only noisy labels but also class imbalance. We propose a simple yet effective method to address noisy labels in imbalanced datasets.
  arXiv  Detail & Related papers  (2024-02-17T10:34:53Z)
• A Novel Hybrid Sampling Framework for Imbalanced Learning [0.0]
  "SMOTE-RUS-NC" has been compared with other state-of-the-art sampling techniques. Rigorous experimentation has been conducted on 26 imbalanced datasets.
  arXiv  Detail & Related papers  (2022-08-20T07:04:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.