To address the issue of class imbalance in datasets

a natural neighborhood hypersphere oversampling method (NNHOS) for high performance classification of imbalanced data sets is proposed in this paper. First

for each sample point in the imbalanced data sets

its natural neighbors are searched until a stable natural neighborhood is formed. Then

based on the label characteristics of the natural neighbors of each sample point

all sample points are classified into five regions: outliers

noise points

safe points of the majority class

safe points of the minority class

and boundary points of the minority class. Subsequently

a hypersphere is constructed for each boundary point of the minority class. At the same time

the small hyperspheres that are completely within the large hypersphere are merged to form a set of hyperspheres. Finally

to achieve a balanced data set

each hypersphere is adaptively assigned a sampling ratio based on the hypersphere radius and a specified number of new sample points are generated within each hypersphere. The results indicate that this method utilizes oversampling on synthetic and real datasets to generate a new sample set. Experiments are conducted using the CART

SVM

and KNN classifiers

and compared with eight other commonly used methods. Additionally

AUC

F1

and Gm are used as evaluation metrics to further demonstrate that this method can more effectively classify imbalanced datasets.