# Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
## 2 Related Work  
To better handle imbalanced data, [PERSON] et al. [31] proposed a developed AdaBoost algorithm called Adaboost-A, which is based on the AUC evaluation metric. In fact, by considering the impact of misclassification probabilities and AUC, the algorithm mentioned above betters the computational performance of the Adaboost algorithm. Furthermore, this study proposed an ensemble learning algorithm named PSOPD-AdaBoost-A, by which the multipliers of Adaboost weak classifiers were optimized. In [32], the authors provide a detailed exploratory comparison between the problem of handling class overlapping and class imbalances using a full range of class overlaps along with a large scale of class imbalance degrees. The rest of this study contains a thorough reviewof the current methods and solutions for handling the imbalanced data classification problem, characterized by two categories: distribution-based and class overlap-based algorithms. The literature [33] proposed an undersampling technique that mainly uses the well-known Naive Bayes classifier. Based on a random primary selection, this classifier is leveraged to select the most informational samples among the existing training dataset. At first, the model is trained on a small training set, and after that, by an iterative teaching method over the current samples, the base model is taught. The practical outcomes showed that the proposed undersampling technique is comparable to other resampling techniques. In [34], [PERSON] et al. proposed a novel deep oversampling method called DeepSMOTE, which contains three principle parts and mainly uses the properties of the effective SMOTE method. Despite being simple, the method is efficient and powerful. An encoder/decoder structure, being SMOTE-based and including an improved loss function, form the three parts of this method. The results of this study show the advantages of the proposed method, especially in GAN-based oversampling cases. The research study [35] investigates the effects of resampling on the performances of multi-class Artificial Neural Networks. Different resampling techniques (both undersampling and oversampling) were examined on several cybersecurity datasets. Furthermore, to determine the results of the proposed methods, various evaluation metrics were used. Finally, four observed patterns were reported that compare the impacts of resampling on the evaluation metrics and model training duration. In another study [36], the authors dealt with the issue of the probable bias and tendency of a learning classifier toward the majority of samples for an imbalanced dataset. This work implemented an innovative three-dimensional framework that includes a discriminator, a generator, and a classifier, together with decision boundary regularization. The remarkable aspect of the proposed method is training a generator in association with a classifier. The reporting results show better performance of the technique than the existing methods. To improve the efficiency and functioning of undersampling methods for imbalanced data, [PERSON] et al. [37] proposed a new undersampling technique that leverages consecutive density peaks to gradually take out samples from the majority class of imbalanced data. In order to determine the importance of the samples of the majority class, two factors were considered, which generate a sequence of samples for learning classifiers. The study compared the implemented algorithm to six well-known undersampling methods over 40 public benchmarks, and the results verify the outperformance of the proposed technique. In order to design learning classifiers that provide stable performances on imbalanced datasets, the study [38] proposed three different methods. These methods are mainly based on genetic algorithms which automatically specify the ratios of samples for oversampling, undersampling, and hybrid sampling techniques. The implemented algorithms were examined on 14 imbalanced datasets, and the results show that they achieved the best AUC compared to random sampling methods.