### Imbalanced Learning  
The class-imbalanced problem is found in many real-world applications, including fraud detection [12], medical diagnosis [13; 14], bioinformatics [15], and so forth. Obtaining class-balanced datasets is very difficult in medical diagnosis because it requires the expensive endoscopy (e.g., laryngoscopy) of patients and labelling with manual supervision. Classifiers trained on imbalanced datasets will reduce their robustness and generalization performance, which is also one of the main challenges of machine learning. Existing solutions to solve class-imbalanced problems can be divided into internal methods and external methods. The internal method [16; 17; 18] uses a variety of cost adjustment techniques in the learning algorithm and makes full use of the initial class-imbalanced dataset during the training process. The external method [19; 20; 21] refers to rebalancing the external training data while keeping the learning algorithm unchanged. Current research [22] shows that external methods are more popular than other methods and are universally applied in various fields. The external method includes oversampling; undersampling; and hybrid undersampling, which combines oversampling and undersampling. The majority of classes are usually undersampled to reduce the number of samples to balance each class's sample distribution. Minority classes are usually oversampled to generate new samples by duplicating or synthesizing the samples to decrease the classes' imbalance ratio. The synthetic minority oversampling technique (SMOTE) [23] is one of the main oversampling methods used to handle imbalanced data. SMOTE is a method to improve Random OverSampling (ROS), reducing the risk of overfitting, but it may cause overgeneralization, is susceptible to generating noise in samples, and causes increased overlapping between different classes [24]. Due to the generation of wrong minority samples, this problem generalizes the minority class region to the majority class region. Therefore, in the current research on imbalanced learning algorithms, SMOTE has multiple variations intended to solve the original algorithm's weaknesses. Many researchers have focused on improving the SMOTE algorithm. The variants of SMOTE that have been proposed are Borderline-SMOTE [25], adaptive synthetic sampling (ADASYN) [26], Random-SMOTE [27], among others. The oversampling algorithm based on clustering works better for imbalanced learning with a large number of unknown samples. On the one hand, clustering algorithms are based on unsupervised learning and are vital elements of machine learning in general. On the other hand, as a soft clustering algorithm, the usefulness of the fuzzy clustering algorithm has been confirmed in existing work [28]. However, in the recent research and application of fuzzy clustering [29; 30], the algorithm has mainly been used as a classification technique. Especially for the pathological voice dataset, which has fuzzy boundaries and overlaps between clusters, fuzzy clustering algorithms can be useful [31]. In imbalanced learning, the indistinct boundaries and overlaps between the clusters affect the sampling technique's performance, resulting in the generation of noisy samples with no information value. Thus, we used this particularity of fuzzy clustering for imbalanced learning as a consolidation technique for oversampling operations. Some imbalanced learning algorithms based on clustering have been proposed in [32; 33]. They can omit the steps of labelling all samples in a set and are more suitable for the imbalanced learning of unknown samples in medical diagnosis. Nonetheless, clustering-based SMOTE algorithms ignore the imbalanced distribution within the sample class [34], which may lead to the generation of non-differentiated minority samples underfitting the classifier.