In recent years, the classification problem of an imbalanced dataset is getting a high
demand in the field of machine learning. The SMOTE (Synthetic Minority Oversampling …

Imbalanced data classification is a challenging issue encountered in many practical
applications. Synthetic minority oversampling technique (SMOTE) and its variants are …

Synthetic Minority Over-sampling Technique (SMOTE) is the most popular over-sampling
method. However, its random nature makes the synthesized data and even imbalanced …

Imbalanced learning problems are a challenge faced by classifiers when data samples have
an unbalanced distribution in each class. Furthermore, the synthetic oversampling method …

Oversampling is an efficient technique in dealing with class-imbalance problem. It
addresses the problem by reduplicating or generating the minority class samples to balance …

Handling an imbalanced class problem is a challenging task in real-world applications. This
problem affects various prediction models that predict only the majority class and fail to …

Oversampling is a promising preprocessing technique for imbalanced datasets which
generates new minority instances to balance the dataset. However, improper generated …

Imbalanced data learning has become a major challenge in data mining and machine
learning. Oversampling is an effective way to re-achieve the balance by generating new …

The oversampling technique in the data preprocessing has been utilized to mitigate the
imbalanced data problem in the real research scenario. This imbalance may reduce the …

Purpose Adequate resources for learning and training the data are an important constraint to
develop an efficient classifier with outstanding performance. The data usually follows a …