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

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...

AI Technical Summary

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 solve the problems of the gray wolf optimization algorithm falling into local optimal solutions and slow convergence speed, etc. Classification and prediction, improving the accuracy of decision-making

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