# Combined Cleaning and Resampling Algorithm for Multi-Class Imbalanced Data with Label Noise

arXiv: https://arxiv.org/abs/2004.03406 · HTML: https://mlsanity.com/paper/2004.03406/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 18 of 18)

- [DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data](https://mlsanity.com/paper/2105.02340/) (2021)
- [Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasets](https://mlsanity.com/paper/2207.05471/) (2022)
- [Imbalanced Big Data Oversampling: Taxonomy, Algorithms, Software, Guidelines and Future Directions](https://mlsanity.com/paper/2107.11508/) (2021)
- [DiagSet: a dataset for prostate cancer histopathological image classification](https://mlsanity.com/paper/2105.04014/) (2021)
- [Efficient Augmentation for Imbalanced Deep Learning](https://mlsanity.com/paper/2207.06080/) (2022)
- [Handling Inter-class and Intra-class Imbalance in Class-imbalanced Learning](https://mlsanity.com/paper/2111.12791/) (2021)
- [RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification](https://mlsanity.com/paper/2105.04009/) (2021)
- [Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning](https://mlsanity.com/paper/2403.02690/) (2024)
- [Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach](https://mlsanity.com/paper/2410.21346/) (2024)
- [Multi-granularity Relabeled Under-sampling Algorithm for Imbalanced Data](https://mlsanity.com/paper/2201.03957/) (2022)
- [A Survey on Small Sample Imbalance Problem: Metrics, Feature Analysis, and Solutions](https://mlsanity.com/paper/2504.14800/) (2025)
- [Multimodal Classification: Current Landscape, Taxonomy and Future Directions](https://mlsanity.com/paper/2109.09020/) (2021)
- [Imbalanced data preprocessing techniques utilizing local data characteristics](https://mlsanity.com/paper/2111.14120/) (2021)
- [COVIDHealth: A Benchmark Twitter Dataset and Machine Learning based Web Application for Classifying COVID-19 Discussions](https://mlsanity.com/paper/2402.09897/) (2024)
- [CAPS: Context-Aware Priority Sampling for Enhanced Imitation Learning in Autonomous Driving](https://mlsanity.com/paper/2503.01650/) (2025)
- [Harnessing Near-Infrared Spectroscopy and Machine Learning for Traceable Classification of Hanwoo and Holstein Beef](https://mlsanity.com/paper/2507.02903/) (2025)
- [Taming Data Challenges in ML-based Security Tasks Using Generative AI](https://mlsanity.com/paper/2507.06092/) (2025)
- [IMOVNO+: A Regional Partitioning and Meta-Heuristic Ensemble Framework for Imbalanced Multi-Class Learning](https://mlsanity.com/paper/2602.20199/) (2026)

## Abstract (arXiv)

The imbalanced data classification is one of the most crucial tasks facing modern data analysis. Especially when combined with other difficulty factors, such as the presence of noise, overlapping class distributions, and small disjuncts, data imbalance can significantly impact the classification performance. Furthermore, some of the data difficulty factors are known to affect the performance of the existing oversampling strategies, in particular SMOTE and its derivatives. This effect is especially pronounced in the multi-class setting, in which the mutual imbalance relationships between the classes complicate even further. Despite that, most of the contemporary research in the area of data imbalance focuses on the binary classification problems, while their more difficult multi-class counterparts are relatively unexplored. In this paper, we propose a novel oversampling technique, a Multi-Class Combined Cleaning and Resampling (MC-CCR) algorithm. The proposed method utilizes an energy-based approach to modeling the regions suitable for oversampling, less affected by small disjuncts and outliers than SMOTE. It combines it with a simultaneous cleaning operation, the aim of which is to reduce the effect of overlapping class distributions on the performance of the learning algorithms. Finally, by incorporating a dedicated strategy of handling the multi-class problems, MC-CCR is less affected by the loss of information about the inter-class relationships than the traditional multi-class decomposition strategies. Based on the results of experimental research carried out for many multi-class imbalanced benchmark datasets, the high robust of the proposed approach to noise was shown, as well as its high quality compared to the state-of-art methods.