Classification forecasting method based on Bayesian network

A Bayesian network, classification prediction technology, applied in the fields of instruments, character and pattern recognition, computer parts, etc., can solve the problem of long learning time, and achieve the effect of saving time and cost

Inactive Publication Date: 2016-10-26
BEIJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0003] Aiming at the problem of too long learning time of the traditional Bayesian network learning algorithm in the case of a large amount of calculation data in the existing technology, there is no effective solution at present

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  • Classification forecasting method based on Bayesian network
  • Classification forecasting method based on Bayesian network
  • Classification forecasting method based on Bayesian network

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Embodiment Construction

[0041] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0042] It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are to distinguish two entities with the same name but different parameters or parameters that are not the same, see "first" and "second" It is only for the convenience of expression, and should not be construed as a limitation on the embodiments of the present invention, which will not be described one by one in the subsequent embodiments.

[0043] According to an embodiment of the present invention, a Bayesian network-based classification prediction method is provided.

[0044] Such asfigure 1 As shown, the Bayesian network-based classification prediction method provided according to an embodiment of the...

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Abstract

The invention discloses a classification forecasting method based on a Bayesian network. The method comprises the following steps: obtaining data to be trained and dividing the data into a plurality of block data; establishing a corresponding Bayesian subnetwork for each block data; and using the Bayesian subnetworks to carry out classification forecasting simultaneously. Through the technical scheme of obtaining a plurality of subnet structures through parallel learning and carrying out classification forecasting simultaneously, time cost of bayesian network learning under the condition of large data quantity is saved, and a better behavior is achieved under a particular data set.

Description

technical field [0001] The present invention relates to classification prediction, in particular, to a classification prediction method based on Bayesian network. Background technique [0002] As a means of representing the causal relationship between random variables, the Bayesian network is considered suitable for tasks such as classification prediction or cause analysis, and the accuracy of classification prediction tasks completed using the Bayesian network model is often indeed high. for other general algorithms. However, due to the space and time complexity of the learning process of Bayesian networks and the difficulty of understanding their structure, Bayesian networks have not been widely used in the context of classification prediction tasks. Take the heuristic algorithm of the max-min hill-climbing algorithm as an example, its time complexity is uncertain. In the case of a large amount of computing data, the convergence time of the algorithm may increase exponen...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/24155
Inventor 孙鹏飞胡婕吴国仕熊秋
Owner BEIJING UNIV OF POSTS & TELECOMM
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