Learning Possibilistic Networks from Data

Jörg Gebhardt, Rudolf Kruse
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:233-244, 1995.

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 this Paper

BibTeX
@InProceedings{pmlr-vR0-gebhardt95a, title = {Learning Possibilistic Networks from Data}, author = {Gebhardt, J{\"{o}}rg and Kruse, Rudolf}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {233--244}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/gebhardt95a/gebhardt95a.pdf}, url = {https://proceedings.mlr.press/r0/gebhardt95a.html}, 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.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Learning Possibilistic Networks from Data %A Jörg Gebhardt %A Rudolf Kruse %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-gebhardt95a %I PMLR %P 233--244 %U https://proceedings.mlr.press/r0/gebhardt95a.html %V R0 %X 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. %Z Reissued by PMLR on 01 May 2022.
APA
Gebhardt, J. & Kruse, R.. (1995). Learning Possibilistic Networks from Data. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:233-244 Available from https://proceedings.mlr.press/r0/gebhardt95a.html. Reissued by PMLR on 01 May 2022.

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