Classification of imbalanced data is a vastly explored issue of the last and present decade
and still keeps the same importance because data are an essential term today and it …

Class imbalance is one of the challenges of machine learning and data mining fields.
Imbalance data sets degrades the performance of data mining and machine learning …

Abstract Background and Objective Medical machine learning (ML) models tend to perform
better on data from the same cohort than on new data, often due to overfitting, or co-variate …

The security of networked systems has become a critical universal issue that influences
individuals, enterprises and governments. The rate of attacks against networked systems …

Insider threats are malicious acts that can be carried out by an authorized employee within
an organization. Insider threats represent a major cybersecurity challenge for private and …

Machine learning classifiers trained on class imbalanced data are prone to overpredict the
majority class. This leads to a larger misclassification rate for the minority class, which in …

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 …

Training classifiers with datasets which suffer of imbalanced class distributions is an
important problem in data mining. This issue occurs when the number of examples …

Highly imbalanced data typically make accurate predictions difficult. Unfortunately, software
defect datasets tend to have fewer defective modules than non-defective modules. Synthetic …

It is inevitable for gear to become damaged, which has a profound effect on the performance
of gear transmission systems. Solving the problem of gear fault detection using artificial …