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Author: Vahid Rezaei Tabar

Affiliation: Allameh Tabataba'i University, Iran, Islamic Republic of

ISBN: 978-989-758-222-6

ISSN: 2184-4313

Keyword(s): Bayesian Network, Factor Analysis, K2 Algorithm, Node Ordering, Communality.

Related Ontology Subjects/Areas/Topics: Bayesian Models ; Pattern Recognition ; Theory and Methods

Abstract: In this paper, we use the Factor Analysis (FA) to determine the node ordering as an input for K2 algorithm in the task of learning Bayesian network structure. For this purpose, we use the communality concept in factor analysis. Communality indicates the proportion of each variable's variance that can be explained by the retained factors. This method is much easier than ordering-based approaches which do explore the ordering space. Because it depends only on the correlation matrix. As well, experimental results over benchmark networks ‘Alarm’ and ‘Hailfinder’ show that our new method has higher accuracy and better degree of data matching.

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Paper citation in several formats:
Rezaei Tabar, V. (2017). A Simple Node Ordering Method for the K2 Algorithm based on the Factor Analysis.In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, ISSN 2184-4313, pages 273-280. DOI: 10.5220/0006095702730280

@conference{icpram17,
author={Vahid Rezaei Tabar.},
title={A Simple Node Ordering Method for the K2 Algorithm based on the Factor Analysis},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={273-280},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006095702730280},
isbn={978-989-758-222-6},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - A Simple Node Ordering Method for the K2 Algorithm based on the Factor Analysis
SN - 978-989-758-222-6
AU - Rezaei Tabar, V.
PY - 2017
SP - 273
EP - 280
DO - 10.5220/0006095702730280

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