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Molecules 2018, 23(7), 1729; https://doi.org/10.3390/molecules23071729

Causal Discovery Combining K2 with Brain Storm Optimization Algorithm

1
School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
2
School of Physics and Electronic Engineering, Hanshan Normal University, Chaozhou 521041, China
3
School of Mathematics and Big Data, Foshan University, Foshan 528000, China
4
School of Software Engineering, South China University of Technology, Guangzhou 510006, China
5
School of Computing Science and Engineering, Vellore Institute of Technology, Vellore-632014, Tamil Nadu, India
*
Author to whom correspondence should be addressed.
Received: 12 May 2018 / Revised: 7 July 2018 / Accepted: 9 July 2018 / Published: 16 July 2018
(This article belongs to the Special Issue Molecular Computing and Bioinformatics)
Full-Text   |   PDF [1676 KB, uploaded 16 July 2018]   |  

Abstract

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. View Full-Text
Keywords: Bayesian causal model; causal direction learning; K2; brain storm optimization Bayesian causal model; causal direction learning; K2; brain storm optimization
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Hong, Y.; Hao, Z.; Mai, G.; Huang, H.; Kumar Sangaiah, A. Causal Discovery Combining K2 with Brain Storm Optimization Algorithm. Molecules 2018, 23, 1729.

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