The application of machine learning to radiological images is an increasingly active
research area that is expected to grow in the next five to ten years. Recent advances in …

Imbalanced data set problem occurs in classification, where the number of instances of one
class is much lower than the instances of the other classes. The main challenge in …

Credit card fraud is one of the biggest cybercrimes faced by users. Intelligent machine
learning based fraudulent transaction detection systems are very effective in real-world …

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 …

In many industrial applications, classification tasks are often associated with imbalanced
class labels in training datasets. Imbalanced datasets can severely affect the accuracy of …

Abstract Background Diabetes Mellitus (DM) has become the third chronic non-
communicable disease that hits patients after tumors, cardiovascular and cerebrovascular …

Imbalanced data is an important problem for medical data analysis. Medical datasets are
often not balanced in their class labels. The traditional classifiers can be seriously affected …

In this paper, a resampling ensemble algorithm is developed focused on the classification
problems for imbalanced datasets. In the method, the small classes are oversampled and …

Objective Hepatic encephalopathy (HE) is among the most common complications of
cirrhosis. Data for cirrhosis with HE is typically unbalanced. Traditional statistical methods …

Abstract The Synthetic Minority Over Sampling TEchnique (SMOTE) is a widely used
technique to balance imbalanced data. In this paper we focus on improving SMOTE in the …