Overview: Algorithm selection is an engineering decision: the wrong choice can freeze a system at scale, regardless of ...

Data clustering and classification have become indispensable for extracting actionable insights from large-scale, heterogeneous datasets characterised by high volume, velocity and variety. Clustering ...

Decision tree regression is a fundamental machine learning technique to predict a single numeric value. A decision tree regression system incorporates a set of virtual if-then rules to make a ...

Computer vision systems combined with machine learning techniques have demonstrated success as alternatives to empirical methods for classification and selection. This study aimed to classify tomatoes ...

Accurate land use/land cover (LULC) classification remains a persistent challenge in rapidly urbanising regions especially, in the Global South, where cloud cover, seasonal variability, and limited ...

A decision tree regression system incorporates a set of if-then rules to predict a single numeric value. Decision tree regression is rarely used by itself because it overfits the training data, and so ...

Abstract: Because of their transparency, interpretability, and efficiency in classification tasks, decision tree algorithms are the foundation of many Business Intelligence (BI) and Analytics ...

Fans have asked, and the Minnesota Timberwolves have finally answered: The trees are back in Minnesota. The Kevin Garnett-era jersey and court — which were most known for their green tree-lined trim — ...

There is indeed a vast literature on the design and analysis of decision tree algorithms that aim at optimizing these parameters. This paper contributes to this important line of research: we propose ...

Physical frailty is a pressing public health issue that significantly increases the risk of disability, hospitalization, and mortality. Early and accurate detection of frailty is essential for timely ...