Background Over-sampling methods based on Synthetic Minority Over-sampling Technique
(SMOTE) have been proposed for classification problems of imbalanced biomedical data …

Introduction Medical diagnosis is a crucial step for patient treatment. However, diagnosis is
prone to bias due to imbalanced datasets. To overcome the imbalanced dataset problem …

In many healthcare applications, datasets for classification may be highly imbalanced due to
the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority …

The algorithm of C4. 5 decision tree has the advantages of high classification accuracy, fast
calculation speed and comprehensible classification rules, so it is widely used for medical …

The problem of imbalanced data classification often exists in medical diagnosis. Traditional
classification algorithms usually assume that the number of samples in each class is similar …

Background An imbalanced dataset is defined as a training dataset that has imbalanced
proportions of data in both interesting and uninteresting classes. Often in biomedical …

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 …

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 …

Background Classification using class-imbalanced data is biased in favor of the majority
class. The bias is even larger for high-dimensional data, where the number of variables …

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 …