Causal network inference method based on kurtosis

A causal network and kurtosis technology, applied in the field of causal network inference based on kurtosis, can solve the problems of initial value sensitivity, local convergence, unreliable measurement standards, etc., and achieve high accuracy, stability, and high recognition rate Effect

Inactive Publication Date: 2017-06-20
GUANGDONG UNIV OF TECH
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Problems solved by technology

[0009] However, the first two ideas are to convert the problem into a function optimization problem, and then use some optimization algorithms to solve it, but they are sensitive to the initial value and are prone to local convergence defects; for the third method, the current variable is compared with the rest The most independent of the residuals of all varia

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  • Causal network inference method based on kurtosis
  • Causal network inference method based on kurtosis
  • Causal network inference method based on kurtosis

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Embodiment Construction

[0028] The specific embodiment of the present invention will be further described below in conjunction with accompanying drawing:

[0029] Such as figure 1 As shown, a causal network inference method based on kurtosis, through the three processes of selecting exogenous variables based on kurtosis, finding out the causal order layer by layer, and using the least square method to cut edges, includes the following steps:

[0030] 1), set the observation data set X=[x 1 ,x 2 ,...x n ], each variable x i (i=1,2,...n) contains p sample data, and sets the causal sequence set K as an empty set;

[0031] 2), calculate each variable x i kurtosis value k for (i=1,2,...n) i (i=1,2,...n), find the maximum kurtosis value max k i The corresponding variable x m , variable x m It is an exogenous variable, and its calculation formula is:

[0032]

[0033]

[0034] Among them, E(x i 4 ) for the variable x i Fourth order center distance, (E(x i 2 )) 2 for x i the square of ...

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Abstract

The invention relates to a causal network inference method based on kurtosis. The method comprises the steps that exogenous variables are selected based on the kurtosis, a causal order is found layer by layer, and a cutting edge is tested by a cutting edge using the least square method, so that a complete causal network is output. The causal network inference method based on kurtosis is not sensitive to the degree of non-Gaussian of disturbance variables and has strong stability, can still be able to maintain a high recognition rate especially when the non-Gaussian is weak, and has low complexity, kurtosis calculation is needed for each variable itself, the method is a direct estimate way, and the causal network recognition accuracy is high.

Description

technical field [0001] The invention relates to the technical field of data mining, in particular to a kurtosis-based causal network inference method. Background technique [0002] Currently, discovering causal relationships from observational data has received extensive attention and has applications in many fields, such as neuroscience, economics, and epidemiology. In the absence of any prior knowledge, traditional causal discovery methods can only find Markov equivalence classes, and intervention experiments are needed to obtain a complete network, but in reality, many variables cannot be intervened. [0003] This problem can be solved well by the linear non-Gaussian acyclic model (LiNGAM) proposed by Shimizu et al., which is able to identify complete causal networks from only observation datasets and has been increasingly studied. [0004] There are three main ideas for the estimation of the linear non-Gaussian acyclic model (LiNGAM) model: [0005] The first is to tra...

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Application Information

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IPC IPC(8): G06F17/30G06N5/04
CPCG06F16/24564G06N5/046
Inventor 谢峰郝志峰蔡瑞初温雯陈薇陈炳丰
Owner GUANGDONG UNIV OF TECH
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