# Self-paced Ensemble for Highly Imbalanced Massive Data Classification

arXiv: https://arxiv.org/abs/1909.03500 · HTML: https://mlsanity.com/paper/1909.03500/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2019.

## Built on by (most cited first, 28 of 28)

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- [Self-paced ensemble learning for speech and audio classification](https://mlsanity.com/paper/2103.11988/) (2021)
- [AIM: Attributing, Interpreting, Mitigating Data Unfairness](https://mlsanity.com/paper/2406.08819/) (2024)
- [Self-paced Multi-grained Cross-modal Interaction Modeling for Referring Expression Comprehension](https://mlsanity.com/paper/2204.09957/) (2022)
- [CLIMB: Class-imbalanced Learning Benchmark on Tabular Data](https://mlsanity.com/paper/2505.17451/) (2025)
- [IMBENS: Ensemble Class-imbalanced Learning in Python](https://mlsanity.com/paper/2111.12776/) (2021)
- [Handling Inter-class and Intra-class Imbalance in Class-imbalanced Learning](https://mlsanity.com/paper/2111.12791/) (2021)
- [Cross-hospital Sepsis Early Detection via Semi-supervised Optimal Transport with Self-paced Ensemble](https://mlsanity.com/paper/2106.10352/) (2021)
- [Exploring the Learning Difficulty of Data Theory and Measure](https://mlsanity.com/paper/2205.07427/) (2022)
- [Predictive Modeling of Hospital Readmission: Challenges and Solutions](https://mlsanity.com/paper/2106.08488/) (2021)
- [BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning](https://mlsanity.com/paper/2406.12222/) (2024)
- [Two-Stage Fine-Tuning: A Novel Strategy for Learning Class-Imbalanced Data](https://mlsanity.com/paper/2207.10858/) (2022)
- [HBO: Hierarchical Balancing Optimization for Fine-Tuning Large Language Models](https://mlsanity.com/paper/2505.12300/) (2025)
- [Multi-granularity Relabeled Under-sampling Algorithm for Imbalanced Data](https://mlsanity.com/paper/2201.03957/) (2022)
- [A Review of Machine Learning Techniques in Imbalanced Data and Future Trends](https://mlsanity.com/paper/2310.07917/) (2023)
- [Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for Imbalanced Medical Classification](https://mlsanity.com/paper/2311.16650/) (2023)
- [A Survey on Small Sample Imbalance Problem: Metrics, Feature Analysis, and Solutions](https://mlsanity.com/paper/2504.14800/) (2025)
- [C2TC: A Training-Free Framework for Efficient Tabular Data Condensation](https://mlsanity.com/paper/2602.21717/) (2026)
- [ASE: Anomaly Scoring Based Ensemble Learning for Imbalanced Datasets](https://mlsanity.com/paper/2203.10769/) (2022)
- [Envelope imbalanced ensemble model with deep sample learning and local-global structure consistency](https://mlsanity.com/paper/2206.13507/) (2022)
- [Rare Event Detection in Imbalanced Multi-Class Datasets Using an Optimal MIP-Based Ensemble Weighting Approach](https://mlsanity.com/paper/2412.13439/) (2024)
- [Predicting the Reliability of an Image Classifier under Image Distortion](https://mlsanity.com/paper/2412.16881/) (2024)
- [Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security Applications](https://mlsanity.com/paper/2504.18771/) (2025)
- [Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced Datasets](https://mlsanity.com/paper/2505.04204/) (2025)
- [MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images](https://mlsanity.com/paper/2505.18741/) (2025)
- [Learning to Annotate Delayed and False AEB Events: A Practical System for Extreme Class Imbalance and Asymmetric Label Noise](https://mlsanity.com/paper/2606.19186/) (2026)

## Abstract (arXiv)

Many real-world applications reveal difficulties in learning classifiers from imbalanced data. The rising big data era has been witnessing more classification tasks with large-scale but extremely imbalance and low-quality datasets. Most of existing learning methods suffer from poor performance or low computation efficiency under such a scenario. To tackle this problem, we conduct deep investigations into the nature of class imbalance, which reveals that not only the disproportion between classes, but also other difficulties embedded in the nature of data, especially, noises and class overlapping, prevent us from learning effective classifiers. Taking those factors into consideration, we propose a novel framework for imbalance classification that aims to generate a strong ensemble by self-paced harmonizing data hardness via under-sampling. Extensive experiments have shown that this new framework, while being very computationally efficient, can lead to robust performance even under highly overlapping classes and extremely skewed distribution. Note that, our methods can be easily adapted to most of existing learning methods (e.g., C4.5, SVM, GBDT and Neural Network) to boost their performance on imbalanced data.