Image classification problems are often class-imbalanced in practice. Such a class
imbalance can negatively affect the classification performance of CNN models. A State-of …

We propose a deep learning-based over-sampling approach to overcome the class
imbalance problem. Our model consists of three major components: 1) A variant of SMOTE …

The classification of real-world problems always consists of imbalanced and multiclass
datasets. A dataset having unbalanced and multiple classes will have an impact on the …

It is impossible to imagine the advancement of modern artificial intelligence systems without
neural network technologies. During the design process researchers are often faced with the …

Class imbalance is a salient problem in both machine learning and data mining realms.
Sampling techniques have become a cornerstone in solving this challenge, as they enable …

Many medical image diagnostic datasets do not provide adequate training data for
conventional learners because some classes are overrepresented while others are …

В процессе проектирования современных нейросетевых моделей исследователи
часто сталкиваются с проблемой недоступности достаточного количества данных для …