Decision Tree Induction Systems: A Bayesian Analysis
Abstract
Decision tree induction systems are being used for knowledge acquisition in noisy domains. This paper develops a subjective Bayesian interpretation of the task tackled by these systems and the heuristic methods they use. It is argued that decision tree systems implicitly incorporate a prior belief that the simpler (in terms of decision tree complexity) of two hypotheses be preferred, all else being equal, and that they perform a greedy search of the space of decision rules to find one in which there is strong posterior belief. A number of improvements to these systems are then suggested.
Cite
Text
Buntine. "Decision Tree Induction Systems: A Bayesian Analysis." Conference on Uncertainty in Artificial Intelligence, 1987. doi:10.1016/0888-613x(88)90129-6Markdown
[Buntine. "Decision Tree Induction Systems: A Bayesian Analysis." Conference on Uncertainty in Artificial Intelligence, 1987.](https://mlanthology.org/uai/1987/buntine1987uai-decision/) doi:10.1016/0888-613x(88)90129-6BibTeX
@inproceedings{buntine1987uai-decision,
title = {{Decision Tree Induction Systems: A Bayesian Analysis}},
author = {Buntine, Wray L.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1987},
pages = {109-128},
doi = {10.1016/0888-613x(88)90129-6},
url = {https://mlanthology.org/uai/1987/buntine1987uai-decision/}
}