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Causal Discovery Combining K2 with Brain Storm Optimization Algorithm

Exploring and detecting the causal relations among variables have shown huge practical values in recent years, with numerous opportunities for scientific discovery, and have been commonly seen as the core of data science. Among all possible causal discovery methods, causal discovery based on a const...

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Autores principales: Hong, Yinghan, Hao, Zhifeng, Mai, Guizhen, Huang, Han, Kumar Sangaiah, Arun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6100085/
https://www.ncbi.nlm.nih.gov/pubmed/30012940
http://dx.doi.org/10.3390/molecules23071729
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author Hong, Yinghan
Hao, Zhifeng
Mai, Guizhen
Huang, Han
Kumar Sangaiah, Arun
author_facet Hong, Yinghan
Hao, Zhifeng
Mai, Guizhen
Huang, Han
Kumar Sangaiah, Arun
author_sort Hong, Yinghan
collection PubMed
description Exploring and detecting the causal relations among variables have shown huge practical values in recent years, with numerous opportunities for scientific discovery, and have been commonly seen as the core of data science. Among all possible causal discovery methods, causal discovery based on a constraint approach could recover the causal structures from passive observational data in general cases, and had shown extensive prospects in numerous real world applications. However, when the graph was sufficiently large, it did not work well. To alleviate this problem, an improved causal structure learning algorithm named brain storm optimization (BSO), is presented in this paper, combining K2 with brain storm optimization (K2-BSO). Here BSO is used to search optimal topological order of nodes instead of graph space. This paper assumes that dataset is generated by conforming to a causal diagram in which each variable is generated from its parent based on a causal mechanism. We designed an elaborate distance function for clustering step in BSO according to the mechanism of K2. The graph space therefore was reduced to a smaller topological order space and the order space can be further reduced by an efficient clustering method. The experimental results on various real-world datasets showed our methods outperformed the traditional search and score methods and the state-of-the-art genetic algorithm-based methods.
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spelling pubmed-61000852018-11-13 Causal Discovery Combining K2 with Brain Storm Optimization Algorithm Hong, Yinghan Hao, Zhifeng Mai, Guizhen Huang, Han Kumar Sangaiah, Arun Molecules Article Exploring and detecting the causal relations among variables have shown huge practical values in recent years, with numerous opportunities for scientific discovery, and have been commonly seen as the core of data science. Among all possible causal discovery methods, causal discovery based on a constraint approach could recover the causal structures from passive observational data in general cases, and had shown extensive prospects in numerous real world applications. However, when the graph was sufficiently large, it did not work well. To alleviate this problem, an improved causal structure learning algorithm named brain storm optimization (BSO), is presented in this paper, combining K2 with brain storm optimization (K2-BSO). Here BSO is used to search optimal topological order of nodes instead of graph space. This paper assumes that dataset is generated by conforming to a causal diagram in which each variable is generated from its parent based on a causal mechanism. We designed an elaborate distance function for clustering step in BSO according to the mechanism of K2. The graph space therefore was reduced to a smaller topological order space and the order space can be further reduced by an efficient clustering method. The experimental results on various real-world datasets showed our methods outperformed the traditional search and score methods and the state-of-the-art genetic algorithm-based methods. MDPI 2018-07-16 /pmc/articles/PMC6100085/ /pubmed/30012940 http://dx.doi.org/10.3390/molecules23071729 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hong, Yinghan
Hao, Zhifeng
Mai, Guizhen
Huang, Han
Kumar Sangaiah, Arun
Causal Discovery Combining K2 with Brain Storm Optimization Algorithm
title Causal Discovery Combining K2 with Brain Storm Optimization Algorithm
title_full Causal Discovery Combining K2 with Brain Storm Optimization Algorithm
title_fullStr Causal Discovery Combining K2 with Brain Storm Optimization Algorithm
title_full_unstemmed Causal Discovery Combining K2 with Brain Storm Optimization Algorithm
title_short Causal Discovery Combining K2 with Brain Storm Optimization Algorithm
title_sort causal discovery combining k2 with brain storm optimization algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6100085/
https://www.ncbi.nlm.nih.gov/pubmed/30012940
http://dx.doi.org/10.3390/molecules23071729
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