Data classification prediction method and system based on improved grey wolf optimizer

An optimization algorithm and data classification technology, applied in the field of big data, it can solve problems such as slow convergence speed, gray wolf optimization algorithm falling into local optimal solution, etc.

Active Publication Date: 2018-04-13
WENZHOU UNIVERSITY
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Problems solved by technology

[0005] The purpose of the embodiments of the present invention is to provide a data classification and prediction method and system based on the improved gray wolf optimization algorithm, which can so

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  • Data classification prediction method and system based on improved grey wolf optimizer
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  • Data classification prediction method and system based on improved grey wolf optimizer

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[0091] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

[0092] like figure 1 As shown, in the embodiment of the present invention, a method for optimizing data classification prediction by improving the gray wolf optimization algorithm proposed, including the steps:

[0093] Step S1, obtaining historical data, and normalizing and classifying the obtained historical data;

[0094] The specific process is to obtain historical data related to the problem to be researched, normalize it and classify it, and use formula (1) to perform standard normalization on it;

[0095]

[0096] The classified attributes include data attributes and category a...

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Abstract

The embodiment of the invention discloses a data classification prediction method and system based on an improved grey wolf optimizer. The method comprises the steps that historical data is acquired,and the acquired historical data is subjected to normalization processing and classification; the historical data obtained after normalization processing is used as training samples of a support vector machine, and the preset improved grey wolf optimizer is utilized to optimize a penalty coefficient and kernel width of the support vector machine; a prediction model is constructed according to theoptimized penalty coefficient and kernel width of the support vector machine; and to-be-predicted data is acquired, the to-be-predicted data is used as to-be-predicted samples to be imported into theprediction model, and classifications of the to-be-predicted data and a predicted value corresponding to each classification are obtained. Through the data classification prediction method and system,the problems that the grey wolf optimizer is trapped in a local optimal solution and is low in convergence speed can be solved, classification and prediction on problems in specific domains are realized, and decision precision is improved.

Description

technical field [0001] The invention relates to the technical field of big data, in particular to a method and a system for optimizing data classification prediction by improving the gray wolf optimization algorithm. Background technique [0002] With the development of technology, the application fields of big data are becoming wider and wider. Therefore, new challenges are raised for the classification and prediction of big data. In particular, the swarm intelligence optimization algorithm is used in the classification and prediction of big data. [0003] It is generally known that the swarm intelligence optimization algorithm achieves the goal of optimization by simulating the swarm intelligence behaviors exhibited by various biological and non-living systems in nature, and using the mutual cooperation and communication among individuals in the group. These swarm intelligence algorithms are more famous: ant colony algorithm, particle swarm algorithm, artificial bee colony...

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Application Information

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IPC IPC(8): G06F17/30G06N3/00G06N3/08
CPCG06F16/285G06F16/35G06N3/006G06N3/08
Inventor 陈慧灵罗杰赵学华蔡振闹童长飞黄辉李俊
Owner WENZHOU UNIVERSITY
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