SVM classifier parameter optimization method based on improved particle swarm algorithm

Abstract

The invention discloses a SVM classifier parameter optimization method based on an improved particle swarm algorithm. The method comprises the following steps: (1) performing 10-fold cross verification on collected sample data, selecting the parameter influencing the classifier performance as the to-be-optimized parameter; (2) initializing related parameters of the classifier and the particle swarm algorithm, and updating particle speed and location according to related parameters; (3) setting the to-be-optimized parameter of the classifier as the corresponding dimension value at the current location of the particle, and computing to obtain the fitness value corresponding to the current location of the particle; and (4) obtaining the fitness value evaluation particle according to the fitness value corresponding to the current location of the particle, and updating the individual optimal location and the population optimal location. Through the method disclosed by the invention, the mixed kernel function based on the polynomial kernel function and the Gaussian kernel function is constructed, the traditional particle swarm algorithm is improved, the kernel function parameter is optimized by utilizing the improved PSO-SVM algorithm, and then the comprehensive performance of the classifier is improved, the generalization capacity of the classifier is improved when the high classification precision is guaranteed.

Description

technical field [0001] The invention relates to a parameter optimization method of an SVM classifier, in particular to a parameter optimization method of an SVM classifier based on an improved particle swarm algorithm. Background technique [0002] Kernel function is the key factor affecting the performance of SVM classifier. Usually we divide kernel functions into two categories: local kernel functions and global kernel functions. The former has weak generalization ability and strong learning ability, while the latter has strong generalization ability and weak learning ability. Among the commonly used kernel functions, the polynomial kernel function belongs to the global kernel function, and the Gaussian kernel function belongs to the local kernel function. The selection of the kernel function includes two steps: the determination of the kernel function and the determination of the parameter value of the kernel function. [0003] For the determination of the kernel funct...

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Problems solved by technology

[0005] Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a method for optimizing parameters of an SVM classifier based on an improved particle swarm optimization algorithm, which can solve the problems of low comprehensive performance of the classifier and poor generalization ability of the classifier

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