Learning Possibilistic Networks from Data
Abstract
We introduce a method for inducing the structure of (causal) possibilistic networks from databases of sample cases. In comparison to the construction of Bayesian belief networks, the proposed framework has some advantages, namely the explicit consideration of imprecise (set-valued) data, and the realization of a controlled form of information compression in order to increase the efficiency of the learning strategy as well as approximate reasoning using local propagation techniques. Our learning method has been applied to reconstruct a non-singly connected network of 22 nodes and 22 arcs without the need of any a priori supplied node ordering.
Cite
Text
Gebhardt and Kruse. "Learning Possibilistic Networks from Data." Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995.Markdown
[Gebhardt and Kruse. "Learning Possibilistic Networks from Data." Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995.](https://mlanthology.org/aistats/1995/gebhardt1995aistats-learning/)BibTeX
@inproceedings{gebhardt1995aistats-learning,
title = {{Learning Possibilistic Networks from Data}},
author = {Gebhardt, Jörg and Kruse, Rudolf},
booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics},
year = {1995},
pages = {233-244},
volume = {R0},
url = {https://mlanthology.org/aistats/1995/gebhardt1995aistats-learning/}
}