# Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
## 2 Related Work
To decrease the domination of the majority class samples, the study [39] implemented a novel hybrid method called CDSMOTE, which uses class decomposition and oversampling on the minority class samples. Contrary to general undersampling algorithms, this proposed method keeps the majority class samples, leading to more balanced data. The algorithm was examined on 60 imbalanced public datasets, and the results show comparable performance compared to the existing algorithms. By introducing a new algorithm called SMOTE-LOF, [PERSON] and [PERSON] [40] attempted to refine SMOTE. This method distinguishes the noise that arises when dealing with imbalanced datasets by adding the Local Outlier Factor (LOF). By examining the proposed algorithm over different imbalanced datasets, the results were compared to SMOTE. Unlike small data samples, for a large-scale dataset with a small imbalance ratio, SMOTE-LOF outperforms SMOTE. In order to annihilate the overlap between the majority class and the minority class in an imbalanced dataset and obtain a balanced and normalized class distribution, the study [29] implemented two innovative density-based methods. These methods were density-based undersampling (DB_US) and density-based hybrid sampling (DB_HS). The first method applies merely an undersampling algorithm, while the second implements both undersampling and oversampling approaches. In addition, the balanced datasets were modeled employing Random Forest (RF) and Support Vector Machine (SVM) classifiers. As a result, the two proposed methods eliminated high-density samples from the majority class and omitted the noises of both classes. The performance of these methods was examined on 16 imbalanced datasets. In the literature [41], the authors proposed a novel classification method called the Bagging Supervised Autoencoder Classifier (BSAC) to model credit scoring problems. This algorithm essentially leverages the superlative implementation of a supervised autoencoder based on the axioms of multi-task learning. Furthermore, BSAC tackles the issue of imbalanced datasets by engaging a variation of the Bagging procedure based on undersampling techniques. The examinations of benchmark and real-world credit scoring datasets show the robustness and efficiency of BSAC. To improve the performance of the basic antlion optimization (ALO), in [42], a novel modified antlion optimization method (MALO) was introduced. This algorithm adds an extra variable that depends on the step size of the ants as revising the antlion position. Furthermore, MALO is modified to the issues of sample reduction to achieve better performance due to various metrics. MALO was examined on several benchmarks and balanced and imbalanced datasets. The results show the outperformance of MALO against the primary ALO method and some other comparable algorithms. In [43], [PERSON] et al., implemented a sampling level technique called the gravitational balanced multiple kernel learning (GBMKL) algorithms, which merges the gravity approach to produce the gravitation-balanced midpoint samples (GBMS) placed on the classification boundary. Moreover, to better the generalization efficiency, the classification boundary was modified according to the nearest neighbors of the boundary (NNB) samples. Finally, two regularization terms that correspond to GBMS and NNB were formulated to prevent overfitting. The resulting method was examined on 54 artificial and real-life imbalanced datasets, and the outcomes show the dominance of the implemented method. [PERSON] et al. in [44] studied the near-miss positive samples in the class of imbalanced datasets. They showed that if the true positive samples are severely limited, the accuracy of the proposed model could be increased by obtaining modified label-like side information positivity to identify near-miss samples from true negatives. Furthermore, the proposed method follows learning using privileged information that leverages side information for training the desired model devoid of predicting the side information itself.