A survey of decision tree classifier methodology

1991/01/01 by S.R. Safavian, D. Landgrebe, D. A. Landgrebe · 3,820 citations
Computer Science · Engineering · #Artificial intelligence #Classifier (UML) #Computer science #Data mining #Decision tree #Decision tree learning #Fault Detection and Control Systems #Feature selection #Incremental decision tree #Machine Learning and ELM #Machine learning #Neural Networks and Applications

paper · open access · doi:10.1109/21.97458

published in IEEE Transactions on Systems Man and Cybernetics 21(3), 660-674 (Institute of Electrical and Electronics Engineers)

openalex publication_date 1991/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

Abstract

A survey is presented of current methods for decision tree classifier (DTC) designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, the subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed. The relation between decision trees and neutral networks (NN) is also discussed.>

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