Abstract Decision Trees (DTs) are predictive models in supervised learning, known not only
for their unquestionable utility in a wide range of applications but also for their interpretability …

In order to meet the green development trend of today's ports and accelerate the formation of
green and low-carbon transportation modes. In this paper, the coordinated scheduling …

Unmanned aerial vehicle (UAV) is one of the preferred tools for coverage detection
missions, because of its maneuverability and flexibility. It is challenging for the UAV to …

In the research area of anomaly detection, novel and promising methods are frequently
developed. However, most existing studies exclusively focus on the detection task only and …

The protection of sensitive data becomes more vital, as data increases in value and potency.
Furthermore, the pressure increases from regulators and society on model developers to …

While new and effective methods for anomaly detection are frequently introduced, many
studies prioritize the detection task without considering the need for explainability. Yet, in …

Finding an optimal decision tree for a supervised learning task is a challenging
combinatorial problem to solve at scale. It was recently proposed to frame the problem as a …

Electronic warfare decision-making involves many kinds of targets, different performances,
complex environments and other issues, which makes it difficult to make real-time decisions …

Decision Trees (DTs) are widely used in various domains for their simplicity and
interpretability. However, traditional DTs often suffer from low accuracy and reduced …

It is extremely useful to exploit labeled datasets not only to learn models and perform
predictive analytics but also to improve our understanding of a domain and its available …