From-bottom-to-top high-dimension-data causal network learning method

A learning method and causal network technology, applied in the field of data mining, can solve the problems of weak causal discovery of the global structure model and insufficient high-dimensional data expression ability of the local structure model

Inactive Publication Date: 2015-04-22
GUANGDONG UNIV OF TECH
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Problems solved by technology

[0005] In order to solve the problems that the global structure model is weak in causal discovery and the local structure model is insufficient in expressing high-dimensional data and relies on strict data generation mechanism assumptions, the present invention establishes a method that combines the global structure inference method with the local structure A Bottom-Up Feasible Framework for Efficient Combination of Inference Methods

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[0009] Corresponding to the three parts of the above method, the present invention is composed of three modules sequentially: a local causal structure generating module, a global directed acyclic graph topology sorting module based on causal strength measurement and a redundant causal relationship elimination module. The specific functions and implementation steps of these three modules are detailed as follows.

[0010] 1. Local causal structure generation module

[0011] Input: sample set D, variable set V, threshold α.

[0012] Output: causal relationship strength map G (including depicting the i-th variable and the j-th variable causal relationship v i → j measure of strength g ij and w ij ).

[0013] 1) Divide the variable set V into q disjoint sets of equal size, namely V 1 , V 2 ,...,V q . q recommended value Where m is the number of samples and n is the number of variables.

[0014] 2) Every two sets V i and V j (allowing i and j to be equal) form a subfie...

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Abstract

The invention discloses a from-bottom-to-top high-dimension-data causal network learning method. The method includes the steps of a causal relationship local structure discovery algorithm, wherein a local causal relation learning method and a causal relationship intensity communication strategy are adopted to learn the local causal relationship intensity relationship among variables; a global variable causal sorting algorithm, wherein on the basis of the biggest loop-free directed subgraph model, high-dimension variable global causal relationship sorting is achieved on the basis of local structure strength measurement and a redundant causal relationship elimination strategy, wherein on the basis of global causal sorting, reliable causal relationship discovery on high-dimension observation data is finally achieved.

Description

technical field [0001] The invention relates to the field of data mining, in particular to a bottom-up causal network learning method oriented to high-dimensional observation data. Background technique [0002] At present, causal inference has been widely used in various fields, typical applications such as biological network inference, disease diagnosis, drug effect analysis, disease-causing gene discovery, social network analysis, etc. The application requirements in these fields have prompted a lot of causal discovery research work, and a large number of causal inference theories and algorithms have emerged. The basis of causal inference theory, algorithm and application is the causal relationship model. The classic causal relationship models include the Rubin Causal Model (Rubin Causal Model; RCM) proposed by Donald Rubin and the Causal Diagram proposed by Judea Pearl. Pearl illustrates the equivalence of the two. The former (Rubin causality model) examines the averag...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N5/00
Inventor 蔡瑞初郝志峰陈薇温雯王丽娟
Owner GUANGDONG UNIV OF TECH
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