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 ...

For example, a decision tree regression model prediction might be, "If employee age is greater than 43.0 and age is less than or equal to 51.5 and years-experience is less than or equal to 20.0 and ...

Abstract: This paper presents a comparative analysis of various decision tree algorithms applied to the task of predicting match outcomes in Defense of the Ancients 2, a complex multiplayer online ...

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 ...

ABSTRACT: The advent of the internet, as we all know, has brought about a significant change in human interaction and business operations around the world; yet, this evolution has also been marked by ...

Abstract: The decision tree algorithm is an effective machine learning technique, but it cannot uncover causal relationships within data. To overcome this limitation, the causal decision tree was ...

Decision tree is an effective supervised learning method for solving classification and regression problems. This article combines the Pearson correlation coefficient with the CART decision tree, ...

QUEST is proposed by Loh and Shih (1997), and stands for Quick, Unbiased, Efficient, Statistical Tree. It is a tree-structured classification algorithm that yields a binary decision tree. A comparison ...