For the last two decades, oversampling has been employed to overcome the challenge of
learning from imbalanced datasets. Many approaches to solving this challenge have been …

近年来不平衡分类问题受到广泛关注. SMOTE 过采样通过添加生成的少数类样本改变不平衡
数据集的数据分布, 是改善不平衡数据分类模型性能的流行方法之一. 本文首先阐述了SMOTE …

Since most classifiers are biased toward the dominant class, class imbalance is a
challenging problem in machine learning. The most popular approaches to solving this …

Detection and classification of acute psychological stress (APS) and physical activity (PA) in
daily lives of people with chronic diseases can provide precision medicine for the treatment …

Heart disease is a serious worldwide health issue with wide-reaching effects. Since heart
disease is one of the leading causes of mortality worldwide, early detection is crucial …

The first year of an engineering student was important to take proper academic planning. All
subjects in the first year were essential for an engineering basis. Student performance …

Oversampling is a strategy employed in machine learning to handle imbalanced datasets by
creating copies of the minority class instances to balance the dataset, thus reducing bias …

The imbalanced datasets and their classification has pulled in as a hot research topic over
the years. It is used in different fields, for example, security, finance, health, and many others …

In healthcare machine learning is used mainly for disease diagnosis or acute condition
detection based on patient data analysis. In the proposed work diabetic patient dataset …

In this paper, we propose an ensemble learning algorithm called under-bagging k-nearest
neighbors (under-bagging k-NN) for imbalanced classification problems. On the theoretical …