# Effective Class-Imbalance learning based on SMOTE and Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2209.00653 · HTML: https://mlsanity.com/paper/2209.00653/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 102 references that are in the map.
- First posted on arXiv 1 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data](https://mlsanity.com/paper/2105.02340/) (2021)
- [Survey of resampling techniques for improving classification performance in unbalanced datasets](https://mlsanity.com/paper/1608.06048/) (2016)
- [Bagging Supervised Autoencoder Classifier for Credit Scoring](https://mlsanity.com/paper/2108.07800/) (2021)
- [GSVMA: A Genetic-Support Vector Machine-Anova method for CAD diagnosis based on Z-Alizadeh Sani dataset](https://mlsanity.com/paper/2108.08292/) (2021)

## Built on by (most cited first, 6 of 6)

- [A Quantum Approach to Synthetic Minority Oversampling Technique (SMOTE)](https://mlsanity.com/paper/2402.17398/) (2024)
- [Pruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging Infrastructure](https://mlsanity.com/paper/2503.14799/) (2025)
- [GAT-RWOS: Graph Attention-Guided Random Walk Oversampling for Imbalanced Data Classification](https://mlsanity.com/paper/2412.16394/) (2024)
- [VR-FuseNet: A Fusion of Heterogeneous Fundus Data and Explainable Deep Network for Diabetic Retinopathy Classification](https://mlsanity.com/paper/2504.21464/) (2025)
- [Malicious URL Detection using optimized Hist Gradient Boosting Classifier based on grid search method](https://mlsanity.com/paper/2406.10286/) (2024)
- [A Novel Double Pruning method for Imbalanced Data using Information Entropy and Roulette Wheel Selection for Breast Cancer Diagnosis](https://mlsanity.com/paper/2503.12239/) (2025)

## Abstract (arXiv)

Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models for achieving satisfactory results. ID is the occurrence of a situation where the quantity of the samples belonging to one class outnumbers that of the other by a wide margin, making such models learning process biased towards the majority class. In recent years, to address this issue, several solutions have been put forward, which opt for either synthetically generating new data for the minority class or reducing the number of majority classes for balancing the data. Hence, in this paper, we investigate the effectiveness of methods based on Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs), mixed with a variety of well-known imbalanced data solutions meaning oversampling and undersampling. To evaluate our methods, we have used KEEL, breast cancer, and Z-Alizadeh Sani datasets. In order to achieve reliable results, we conducted our experiments 100 times with randomly shuffled data distributions. The classification results demonstrate that the mixed Synthetic Minority Oversampling Technique (SMOTE)-Normalization-CNN outperforms different methodologies achieving 99.08% accuracy on the 24 imbalanced datasets. Therefore, the proposed mixed model can be applied to imbalanced binary classification problems on other real datasets.