Purpose: We aimed to establish and validate a risk assessment system that combines
demographic and clinical variables to predict the 3-year risk of incident diabetes in Chinese …

In ensemble learning, it is necessary to build a balancing mechanism to balance the
accuracy of individual learners with the diversity between individual learners to achieve …

SAR image registration is a crucial problem in SAR image processing since the registration
results with high precision are conducive to improving the quality of other problems, such as …

Ensemble methods are widely used to tackle class imbalance problem. However, for
existing imbalanced ensemble (IE) methods, the samples in each subset are resampled …

Deep ensemble models have been demonstrated to show promising generalization
capability. A deep ensemble model includes several deep neural networks as base …

Ensemble learning has emerged as a powerful approach for disease diagnosis, combining
multiple classifiers to enhance predictive accuracy and robustness. Nevertheless, the …

For existing imbalanced ensemble (IE) methods, the sample subsets are constructed from
the same dataset, which usually suffer from low quality (diversity and separability) of the …

As an important component of the transmission system, planetary gearboxes are widely
used in equipment such as aircraft, wind turbines, etc. The changing operating conditions …

In ensemble learning, accuracy and diversity are the main factors affecting its performance.
In previous studies, diversity was regarded only as a regularization term, which does not …

Ensemble learning techniques are widely applied to classification tasks such as credit-risk
evaluation. As for most credit-risk evaluation scenarios in the real world, only imbalanced …