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 …

Deep neural networks have been widely used in battery health management, including state-
of-health (SOH) estimation and remaining useful life (RUL) prediction, with great success …

Black-box algorithms are among the dominant mangrove map** approaches with
complex decision-making procedures. Model internals and tacit knowledge were neglected …

Classification and regression trees, as well as their variants, are off-the-shelf methods in
Machine Learning. In this paper, we review recent contributions within the Continuous …

The ever-growing volume of IoT traffic brings challenges to IoT anomaly detection systems.
Existing anomaly detection systems perform all traffic detection on the control plane, which …

The water quality of lakes recharged by reclaimed water is affected by both the fluctuation of
reclaimed water quality and the biochemical processes in the lakes, and therefore the main …

Random forests have long been considered as powerful model ensembles in machine
learning. By training multiple decision trees, whose diversity is fostered through data and …

State-of-the-art learning algorithms, such as random forests or neural networks, are often
qualified as “black-boxes” because of the high number and complexity of operations …

Given the complexity and lack of transparency in deep neural networks (DNNs), extensive
efforts have been made to make these systems more interpretable or explain their behaviors …

Although interaction effects can be exploited to improve predictions and allow for valuable
insights into covariate interplay, they are given limited attention in analysis. Interaction …