Next Article in Journal
Solvent Extraction and Identification of Active Anticariogenic Metabolites in Piper cubeba L. through 1H-NMR-Based Metabolomics Approach
Next Article in Special Issue
The Cartesian Product and Join Graphs on Edge-Version Atom-Bond Connectivity and Geometric Arithmetic Indices
Previous Article in Journal
Development of Conjugate Addition of Lithium Dialkylcuprates to Thiochromones: Synthesis of 2-Alkylthiochroman-4-ones and Additional Synthetic Applications
Previous Article in Special Issue
Scoring Amino Acid Mutations to Predict Avian-to-Human Transmission of Avian Influenza Viruses
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Molecules 2018, 23(7), 1729;

Causal Discovery Combining K2 with Brain Storm Optimization Algorithm

School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
School of Physics and Electronic Engineering, Hanshan Normal University, Chaozhou 521041, China
School of Mathematics and Big Data, Foshan University, Foshan 528000, China
School of Software Engineering, South China University of Technology, Guangzhou 510006, China
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]   |  


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Hong, Y.; Hao, Z.; Mai, G.; Huang, H.; Kumar Sangaiah, A. Causal Discovery Combining K2 with Brain Storm Optimization Algorithm. Molecules 2018, 23, 1729.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Molecules EISSN 1420-3049 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top