# Imbalanced data preprocessing techniques utilizing local data characteristics

arXiv: https://arxiv.org/abs/2111.14120 · HTML: https://mlsanity.com/paper/2111.14120/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 69 references that are in the map.
- First posted on arXiv 28 Nov 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data](https://mlsanity.com/paper/1508.03422/) (2015)
- [Imbalanced Deep Learning by Minority Class Incremental Rectification](https://mlsanity.com/paper/1804.10851/) (2018)
- [Radial-Based Undersampling for Imbalanced Data Classification](https://mlsanity.com/paper/1906.00452/) (2019)
- [Combined Cleaning and Resampling Algorithm for Multi-Class Imbalanced Data with Label Noise](https://mlsanity.com/paper/2004.03406/) (2020)
- [CSMOUTE: Combined Synthetic Oversampling and Undersampling Technique for Imbalanced Data Classification](https://mlsanity.com/paper/2004.03409/) (2020)
- [RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification](https://mlsanity.com/paper/2105.04009/) (2021)
- [Potential Anchoring for imbalanced data classification](https://mlsanity.com/paper/2104.08548/) (2021)

## Abstract (arXiv)

Data imbalance, that is the disproportion between the number of training observations coming from different classes, remains one of the most significant challenges affecting contemporary machine learning. The negative impact of data imbalance on traditional classification algorithms can be reduced by the data preprocessing techniques, methods that manipulate the training data to artificially reduce the degree of imbalance. However, the existing data preprocessing techniques, in particular SMOTE and its derivatives, which constitute the most prevalent paradigm of imbalanced data preprocessing, tend to be susceptible to various data difficulty factors. This is in part due to the fact that the original SMOTE algorithm does not utilize the information about majority class observations. The focus of this thesis is development of novel data resampling strategies natively utilizing the information about the distribution of both minority and majority class. The thesis summarizes the content of 12 research papers focused on the proposed binary data resampling strategies, their translation to the multi-class setting, and the practical application to the problem of histopathological data classification.