Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …

In severely imbalanced datasets, using traditional binary or multi-class classification typically
leads to bias towards the class (es) with the much larger number of instances. Under such …

Due to the extensive use of computer networks, new risks have arisen, and improving the
speed and accuracy of security mechanisms has become a critical need. Although new …

In ordinary credit card datasets, there are far fewer fraudulent transactions than ordinary
transactions. In dealing with the credit card imbalance problem, the ideal solution must have …

Breast cancer is a disease that affects women in different countries around the world. The
real cause of breast cancer is particularly challenging to determine, and early detection of …

Turkish small-and medium-sized enterprises (SMEs) are exposed to fraud risks and creditor
banks are facing big challenges to deal with financial accounting fraud. This study explores …

In the domain of Medicare insurance fraud detection, handling imbalanced Big Data and
high dimensionality remains a significant challenge. This study assesses the combined …

Excessive concentrations of Ni in soil have many severe effects, negatively affecting human
health and leading to disease, while also posing a threat to animals and plants. Although the …

Feature selection plays a pivotal role in preprocessing data for machine learning (ML)
models. It entails choosing a subset of pertinent features to enhance the model's accuracy …

Advancement of high-throughput technologies in omics studies had produced large amount
of information that enables integrated analysis of complex diseases. Complex diseases such …