TY - JOUR
AU - Murthy, Sreerama
AB - Decision trees have proved to be valuable tools for the description, classification and generalization of data. Work on constructing decision trees from data exists in multiple disciplines such as statistics, pattern recognition, decision theory, signal processing, machine learning and artificial neural networks. Researchers in these disciplines, sometimes working on quite different problems, identified similar issues and heuristics for decision tree construction. 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.
TI - Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
JF - Data Mining and Knowledge Discovery
DO - 10.1023/A:1009744630224
DA - 2004-09-29
UR - https://www.deepdyve.com/lp/springer-journals/automatic-construction-of-decision-trees-from-data-a-multi-CNWc5eJW4n
SP - 345
EP - 389
VL - 2
IS - 4
DP - DeepDyve
ER -