Causal Discovery Combining K2 with Brain Storm Optimization Algorithm

Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry
Yinghan HongArun Kumar Sangaiah

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 expe...Continue Reading

References

Aug 17, 2010·Journal of Theoretical Biology·D N GeorgiouA Torres
Apr 6, 2011·IEEE Transactions on Pattern Analysis and Machine Intelligence·Jonas PetersBernhard Scholkopf
Mar 19, 2013·Neural Networks : the Official Journal of the International Neural Network Society·Ruichu CaiZhifeng Hao
May 11, 2016·IEEE Transactions on Neural Networks and Learning Systems·Ruichu CaiMarianne Winslett

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Software Mentioned

K2
GA
Bingo Cloud
BSO
Random
ANM

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