A data classification prediction method and system based on improved gray wolf optimization algorithm

Abstract

The embodiment of the present invention discloses a data classification prediction method and system based on the improved gray wolf optimization algorithm, including obtaining historical data, and normalizing and classifying the obtained historical data; The historical data of the support vector machine is used as the training sample of the support vector machine, and the penalty coefficient and the kernel width of the support vector machine are optimized by using the preset improved gray wolf optimization algorithm; according to the penalty coefficient and the kernel width optimized by the support vector machine, construct Prediction model: acquire the data to be tested, and import the data to be tested into the prediction model as samples to be tested, and obtain the classification of the data to be tested and the corresponding prediction value of each classification. The implementation of the invention can solve the problems of gray wolf optimization algorithm falling into local optimal solution and slow convergence speed, realize classification and prediction of problems in specific fields, and improve decision-making accuracy.

Description

technical field [0001] The present invention relates to the technical field of big data, in particular to a method and system for optimizing data classification prediction by improving gray wolf optimization algorithm. Background technique [0002] With the development of technology, the field of big data application is becoming wider and wider, so new challenges are raised for the classification and prediction of big data, especially the swarm intelligence optimization algorithm is used in the classification and prediction of big data. [0003] As we all know, the swarm intelligence optimization algorithm simulates the swarm intelligence behaviors of various biological and non-living systems in nature, and uses the mutual cooperation and communication between individuals in the group to achieve the purpose of optimization. These swarm intelligence algorithms are more famous: ant colony algorithm, particle swarm algorithm, artificial bee colony algorithm, chicken swarm algor...

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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 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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