EVOLUTIONARY ALGORITHM FOR LEARNING BAYESIAN STRUCTURES FROM DATA

In this paper we report an evolutionary approach to learning Bayesian networks from data. We explain reasons, which advocate such a non-deterministic approach. We analyze weaknesses of previous works and come to conclusion that we should operate in the search space native for the problem i.e. in th...

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Main Authors: MAREK KOZŁOWSKI, SŁAWOMIR T. WIERZCHOŃ
Format: Article
Language:English
Published: Gdańsk University of Technology 2002-07-01
Series:TASK Quarterly
Subjects:
Online Access:https://journal.mostwiedzy.pl/TASKQuarterly/article/view/2248
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author MAREK KOZŁOWSKI
SŁAWOMIR T. WIERZCHOŃ
author_facet MAREK KOZŁOWSKI
SŁAWOMIR T. WIERZCHOŃ
author_sort MAREK KOZŁOWSKI
collection DOAJ
description In this paper we report an evolutionary approach to learning Bayesian networks from data. We explain reasons, which advocate such a non-deterministic approach. We analyze weaknesses of previous works and come to conclusion that we should operate in the search space native for the problem i.e. in the space of directed acyclic graphs instead of standard space of binary strings. This requires adaptation of evolutionary methodology into very specific needs. We propose quite new data representation and implementation of generalized genetic operators and then we present an efficient algorithm capable of learning complex networks without additional assumptions. We discuss results obtained with this algorithm. The approach presented in this paper can be extended with the possibility to absorb some suggestions from experts or obtained by means of data preprocessing.
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spelling doaj.art-48beade7edbd4acc893ec7ca6eec39f72022-12-22T00:40:19ZengGdańsk University of TechnologyTASK Quarterly1428-63942002-07-0163EVOLUTIONARY ALGORITHM FOR LEARNING BAYESIAN STRUCTURES FROM DATAMAREK KOZŁOWSKI0SŁAWOMIR T. WIERZCHOŃ1Warsaw University of Technology, Faculty of Mathematics and Information SciencesPolish Academy of Sciences, Institute of Computer Science; Technical University of Bialystok, Department of Computer Science In this paper we report an evolutionary approach to learning Bayesian networks from data. We explain reasons, which advocate such a non-deterministic approach. We analyze weaknesses of previous works and come to conclusion that we should operate in the search space native for the problem i.e. in the space of directed acyclic graphs instead of standard space of binary strings. This requires adaptation of evolutionary methodology into very specific needs. We propose quite new data representation and implementation of generalized genetic operators and then we present an efficient algorithm capable of learning complex networks without additional assumptions. We discuss results obtained with this algorithm. The approach presented in this paper can be extended with the possibility to absorb some suggestions from experts or obtained by means of data preprocessing. https://journal.mostwiedzy.pl/TASKQuarterly/article/view/2248Bayesian networksstructure learningevolutionary algorithmdiscrete optimization
spellingShingle MAREK KOZŁOWSKI
SŁAWOMIR T. WIERZCHOŃ
EVOLUTIONARY ALGORITHM FOR LEARNING BAYESIAN STRUCTURES FROM DATA
TASK Quarterly
Bayesian networks
structure learning
evolutionary algorithm
discrete optimization
title EVOLUTIONARY ALGORITHM FOR LEARNING BAYESIAN STRUCTURES FROM DATA
title_full EVOLUTIONARY ALGORITHM FOR LEARNING BAYESIAN STRUCTURES FROM DATA
title_fullStr EVOLUTIONARY ALGORITHM FOR LEARNING BAYESIAN STRUCTURES FROM DATA
title_full_unstemmed EVOLUTIONARY ALGORITHM FOR LEARNING BAYESIAN STRUCTURES FROM DATA
title_short EVOLUTIONARY ALGORITHM FOR LEARNING BAYESIAN STRUCTURES FROM DATA
title_sort evolutionary algorithm for learning bayesian structures from data
topic Bayesian networks
structure learning
evolutionary algorithm
discrete optimization
url https://journal.mostwiedzy.pl/TASKQuarterly/article/view/2248
work_keys_str_mv AT marekkozłowski evolutionaryalgorithmforlearningbayesianstructuresfromdata
AT sławomirtwierzchon evolutionaryalgorithmforlearningbayesianstructuresfromdata