Abstract-Decision trees are considered to be one of the most popular approaches for representing classifiers. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining considered the issue of growing a decision tree from available data. This paper presents an updated survey of current methods for constructing decision tree classifiers in a top-down manner. The paper suggests a unified algorithmic framework for presenting these algorithms and describes the various splitting criteria and pruning methodologies.
Most of the oblique DT induction algorithms construct DTs in a top-down fashion REF .
Top-down induction of decision trees classifiers - a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)
IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)
Computer Science