# A Novel Double Pruning method for Imbalanced Data using Information Entropy and Roulette Wheel Selection for Breast Cancer Diagnosis

arXiv: https://arxiv.org/abs/2503.12239 · HTML: https://mlsanity.com/paper/2503.12239/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 114 references that are in the map.
- First posted on arXiv 15 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data](https://mlsanity.com/paper/2105.02340/) (2021)
- [k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)](https://mlsanity.com/paper/2004.04523/) (2020)
- [Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence](https://mlsanity.com/paper/2104.08736/) (2021)
- [Radial-Based Undersampling for Imbalanced Data Classification](https://mlsanity.com/paper/1906.00452/) (2019)
- [Enhancing Quantum Support Vector Machines through Variational Kernel Training](https://mlsanity.com/paper/2305.06063/) (2023)
- [Precision-Recall Curve (PRC) Classification Trees](https://mlsanity.com/paper/2011.07640/) (2020)
- [CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification](https://mlsanity.com/paper/1712.04356/) (2017)
- [GGNB: Graph-Based Gaussian Naive Bayes Intrusion Detection System for CAN Bus](https://mlsanity.com/paper/2108.10908/) (2021)
- [Effective Class-Imbalance learning based on SMOTE and Convolutional Neural Networks](https://mlsanity.com/paper/2209.00653/) (2022)
- [AdaCC: Cumulative Cost-Sensitive Boosting for Imbalanced Classification](https://mlsanity.com/paper/2209.08309/) (2022)

## Abstract (arXiv)

Accurate illness diagnosis is vital for effective treatment and patient safety. Machine learning models are widely used for cancer diagnosis based on historical medical data. However, data imbalance remains a major challenge, leading to hindering classifier performance and reliability. The SMOTEBoost method addresses this issue by generating synthetic data to balance the dataset, but it may overlook crucial overlapping regions near the decision boundary and can produce noisy samples. This paper proposes RE-SMOTEBoost, an enhanced version of SMOTEBoost, designed to overcome these limitations. Firstly, RE-SMOTEBoost focuses on generating synthetic samples in overlapping regions to better capture the decision boundary using roulette wheel selection. Secondly, it incorporates a filtering mechanism based on information entropy to reduce noise, and borderline cases and improve the quality of generated data. Thirdly, we introduce a double regularization penalty to control the synthetic samples proximity to the decision boundary and avoid class overlap. These enhancements enable higher-quality oversampling of the minority class, resulting in a more balanced and effective training dataset. The proposed method outperforms existing state-of-the-art techniques when evaluated on imbalanced datasets. Compared to the top-performing sampling algorithms, RE-SMOTEBoost demonstrates a notable improvement of 3.22% in accuracy and a variance reduction of 88.8%. These results indicate that the proposed model offers a solid solution for medical settings, effectively overcoming data scarcity and severe imbalance caused by limited samples, data collection difficulties, and privacy constraints.