Cause-and-effect structure learning method based on flow characteristics

Abstract

The invention discloses a cause-and-effect structure learning method based on flow characteristics. The method comprises following steps of 1: generating and distributing any new characteristic in a flow manner; 2: carrying out a correlation analysis on each of newly generated characteristics; 3: carrying out a redundancy verification analysis on a characteristic set; 4: carrying out searching orientation based on each of the characteristics; 5: repeating steps of 1-4 until the numbers of the generated characteristics exceed a limit value, finally obtaining a corresponding cause-and-effect structure. According to the invention, a cause-and-effect structure relation can be found in linearly randomly distributed data with flow characteristics and time complexity of learning is reduced, thereby satisfying timeliness requirements of online learning.

Description

technical field [0001] The invention belongs to the field of data mining, in particular to a flow feature-based causal structure learning method for linear arbitrary distribution data. Background technique [0002] With the progress of society and the development of science and technology, the things people need to know become more and more complex. The causal relationship within the system exists objectively. The causal structure learning is to mine the causal structural relationship contained in the data, which can help people understand the complex The nature and laws of things. Causal structure learning has penetrated into various disciplines such as biology, medicine, economics, automatic control, and information processing, and involves various aspects such as daily life, industrial production, and military defense. [0003] The distribution of many variables in real life is often non-Gaussian. For example: the values ​​of magnetoencephalographic (MEG, magnetoencepha...

AI Technical Summary

Problems solved by technology

[0006] The current classical causal structure learning methods cannot effectively deal with continuous data with linear and arbitrary distributions with flow characteristics. The main limitations of these methods include:

[0007] (1) Most of the linear arbitrary distribution-oriented structural learning algorithms are methods based on dependency analysis. In order to determine whether two features are related, this method needs to perform independence tests on a large number of subsets, resulting in the need for more independence tests. Therefore, it takes a lot of time and the computational complexity is relatively large;

[0008] (2) The structure learning algorithm for linear arbitrary distribution generally assumes that all data can be obtained in advance, and cannot process data with flow characteristics, that is, the features flow in one by one, so it cannot effectively deal with the causal structure learning problem in the dynamic and unknown feature space.

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