The application of Artificial Intelligence (AI) across a wide range of domains comes with both
high expectations of its benefits and dire predictions of misuse. While AI systems have …

Abstract Machine learning (ML) models are nowadays used in complex applications in
various domains, such as medicine, bioinformatics, and other sciences. Due to their black …

Visual analytics systems integrate interactive visualizations and machine learning to enable
expert users to solve complex analysis tasks. Applications combine techniques from various …

In machine learning (ML), ensemble methods-such as bagging, boosting, and stacking-are
widely-established approaches that regularly achieve top-notch predictive performance …

Objective: To provide a human–Artificial Intelligence (AI) interaction review for Machine
Learning (ML) applications to inform how to best combine both human domain expertise and …

The base learners and labeled samples (shots) in an ensemble few-shot classifier greatly
affect the model performance. When the performance is not satisfactory, it is usually difficult …

The performance of deep learning models is dependent on the precise configuration of
many layers and parameters. However, there are currently few systematic guidelines for how …

Comparing text documents is an essential task for a variety of applications within diverse
research fields, and several different methods have been developed for this. However …

Stacked generalization (also called stacking) is an ensemble method in machine learning
that uses a metamodel to combine the predictive results of heterogeneous base models …

During the training phase of machine learning (ML) models, it is usually necessary to
configure several hyperparameters. This process is computationally intensive and requires …