Method and device for optimizing FKNN model parameters based on variation Sashimi swarm algorithm

A salp group and model parameter technology, applied in the computer field, achieves the effects of high classification accuracy, strong ability, and high convergence accuracy

Pending Publication Date: 2020-12-22
WENZHOU UNIVERSITY
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Problems solved by technology

[0004] However, using the existing search algorithm to deal with the FKNN model parameter pair (k, m) optimization problem, it is necessary to further improve the convergence speed

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  • Method and device for optimizing FKNN model parameters based on variation Sashimi swarm algorithm
  • Method and device for optimizing FKNN model parameters based on variation Sashimi swarm algorithm
  • Method and device for optimizing FKNN model parameters based on variation Sashimi swarm algorithm

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[0037]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.

[0038]Such asfigure 1As shown in the first embodiment of the present invention, a method for optimizing FKNN model parameters based on the mutant salvage swarm algorithm is proposed, and the method includes the following steps:

[0039]Step S1: obtaining sample data and normalizing the obtained sample data;

[0040]The specific process is that the sample data comes from a variety of different fields and can be designed according to actual needs, such as the medical field and the financial field. The data attribute categories are divided into data attributes and category attributes. For example, for a single sample attribute of breast cancer disease data, the data attribute value is divided into two categories, namely data attribute X1-X9Represents the relevant medical patholog...

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Abstract

The invention provides a method for optimizing parameters of a fuzzy k-nearest neighbor (FKNN) model based on a variation gall sea squirt swarm algorithm. The method comprises the following steps: acquiring sample data and normalizing the acquired sample data; optimizing the parameters k, m of the FKNN by using an integrated variation Sashimi swarm algorithm of a preset restart mechanism; and optimizing the FKNN model by using the optimal neighbor number k and the fuzzy intensity coefficient m value, and predicting the test data based on 10-fold cross validation. By implementing the method, the convergence speed and convergence precision of the algorithm can be improved, and the ability of the algorithm to escape from the local optimal solution is improved, so that a better global approximate optimal solution is found.

Description

Technical field[0001]The present invention relates to the field of computer technology, in particular to a method and device for optimizing FKNN (fuzzy K-nearest neighbor) model parameters based on a variant salvia group algorithm.Background technique[0002]In the real world, there are many practical application problems that can be abstracted into actual parameter optimization problems, and corresponding mathematical models can be established. When solving problems, people often hope to find the best solution as soon as possible. Although traditional mathematical optimization methods can solve some optimization problems, they must meet the following two requirements: 1. The problem must be convex; 2. The final solution is closely related to the initial point. Therefore, scholars are trying to find other efficient algorithms. As an effective method, meta-heuristic algorithm has been proved to be able to find the optimal solution or approximate optimal solution of the optimization pro...

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Application Information

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IPC IPC(8): G06K9/62G06N3/00
CPCG06N3/006G06F18/24147
Inventor 吴述彪陈慧灵王智岩张乐君赵学华谷志阳蔡振闹陈一鹏
Owner WENZHOU UNIVERSITY
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