# Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
## 6 Conclusions and Future Work  
An unbalanced dataset of the majority and minority classes is a challenging issue when samples belonging to one or more classes are not evenly distributed. Especially, imbalanced datasets reasons deep learning-based models to obtain biased results for binary classification. To address this issue, we presented oversampling and undersampling techniques such as SMOTE, TL, OSS, NearMiss, ROS, and RUS. Among these techniques, SMOTE is the most common robust that targets the growth of the amount of minority class samples by generating synthetic samples, which is employed for balancing datasets with an extremely unbalanced ratio. In this study, six deep learning-based models were used to classify the majority and minority classes. We investigated SMOTE + NORM. + CNN/DNN, TL + NORM. + CNN/DNN, OSS + NORM. + CNN/DNN, NearMiss + NORM. + CNN/DNN, ROS + NORM. + CNN/DNN, and RUS + NORM. + CNN/DNN. To evaluate these models, we utilized KEEL, breast cancer, and Z-Alizadeh Sani datasets. The results show that the mixed SMOTE-NORM-CNN model significantly outperforms other models achieving 99.08% accuracy, 99.09% precision, 99.08% sensitivity, 99.09% F1-score, 99.08% G-Mean, 99.03% specificity, 99.08% AUC, and 98.92% kappa on 24 imbalanced datasets. Furthermore, the proposed model has been compared to the study [29], and the mixed model is suitable for the same dataset. Furthermore, we investigated the related methodologies on the Z-Alizadeh Sani dataset. The results indicate that our proposed methodology is more robust. In the future, we are planning to extend our technique to deal with multi-class problems. Moreover, we shall try to exploit different versions of SMOTE (e.g., SMOTE-Cov) with generative DL models, including Variational Autoencoder (VAE) and a Generative Adversarial Network (GAN), to explore their effectiveness in comparison to our model on other real datasets. Our model's major trade-off/limitation is that it has a longer training time and higher computational cost than traditional ML techniques.  
[PERSON] designed the study. [PERSON], [PERSON] and [PERSON] performed conceptualization and methodology. The software and analysis of data have been performed by [PERSON] Furthermore, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] wrote the original draft and visualized the figures. The final manuscript has been reviewed and edited by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
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[1]**Data Availability Statement:** These links for datasets: [[https://sci2s.ugr.es/keel/imbalanced.php](https://sci2s.ugr.es/keel/imbalanced.php)]([https://sci2s.ugr.es/keel/imbalanced.php](https://sci2s.ugr.es/keel/imbalanced.php)) (accessed on 17 March 2023), [[https://www.kdd.org/kdd-cup](https://www.kdd.org/kdd-cup)]([https://www.kdd.org/kdd-cup](https://www.kdd.org/kdd-cup)) (accessed on 17 March 2023), [[https://archive.ics.uci.edu/ml/datasets/[PERSON]+Sani](https://archive.ics.uci.edu/ml/datasets/[PERSON]+Sani)]([https://archive.ics.uci.edu/ml/datasets/[PERSON]+Sani](https://archive.ics.uci.edu/ml/datasets/[PERSON]+Sani)) (accessed on 17 March 2023).  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.