data parallelism for a decision tree, 489–492 data set structure, 479–480 decision tree algorithm, 480–481 decision tree classification, 477–480 processes, 480–488 structure, 478–479 result parallelism for the decision tree, 492–495

Label Semantics is a random set based framework for modeling imprecise concepts where the degree of appropriateness of a linguistic expression as a description of a certain value is measured in terms of how the set of appropriate labels for that value varies across a population. An approach to decision tree induction based on this framework was studied. A new decision tree learning algorithm wa...

We describe the two most commonly used systems for induction of decision trees for classiication: C4.5 and CART. We highlight the methods and diier-ent decisions made in each system with respect to splitting criteria, pruning, noise handling, and other diierentiating features. We describe how rules can be derived from decision trees and point to some diierence in the induction of regression tre...