Decision Tree Classification.

TL;DR: A systematic review of 24 ML-based approaches for identifying and classifying non-functional requirements in requirements documents finds that while ML- based approaches have the potential in the classification and identification of NFRs, they face some open challenges that will affect their performance and practical application.

TL;DR: The main contributions of the paper are to establish the nature of links between the environment and the epidemic, and to highlight those risky environments when the public awareness of the problem and the prevention policies are absolutely necessary for mitigation of the propagation and emergence of the epidemic.

TL;DR: A new model of data preprocessing based on a novel distributed computing architecture focused on large-scale datasets such as UGR’16 is presented and the adequateness of decision tree algorithms for training a machine learning model is shown by using a large dataset when compared with a multilayer perceptron neural network.

TL;DR: In this paper , the authors proposed a computer vision-based system for spotting potholes based on the image segmentation method, followed by calculating the damage ratio. And the results confirm that the proposed models have the potential in predicting and detecting pothole occurrence.

TL;DR: This paper surveys existing work on decision tree construction, attempting to identify the important issues involved, directions the work has taken and the current state of the art.

TL;DR: In this article, a tree learning algorithm can be derived from Bayesian decision theory, which introduces Bayesian techniques for splitting, smoothing, and tree averaging, similar to Quinlan's information gain splitting rule, while smoothing and averaging replace pruning.

TL;DR: It is shown that for most cases, the construction of the storage optimal decision tree is an NP-complete problem, and therefore a heuristic approach to the problem is necessary.

TL;DR: This paper introduces several efficient heuristic sequential inspection procedures for solving the problem of minimizing the expected cost of computing the correct value of a discrete-valued function when it is costly to determine (“inspect”) the values of its arguments.