Method for optimizing disaggregated model by adopting genetic algorithm

Abstract

The invention discloses a method for optimizing a disaggregated model by adopting genetic algorithm. The method comprises the following steps of: 1, acquiring a training sample, wherein the acquiring process includes signal acquisition, characteristic extraction and sample acquisition; 2, selecting kernel function: radial basic function is selected as the kernel function of a disaggregated model needing to be established, and the disaggregated model is a support vector machine model; and 3, determining penalty parameter C and kernel parameter gamma: a genetic algorithm is adopted to optimize the penalty parameter C of the disaggregated model needing to be established and the kernel parameter gamma of the selected radial basic function and the optimization process includes population initialization, calculation on the fitness value of each individual in the initialized population, selection operation, interlace operation and variation operation, calculation on the fitness value of each individual in the offspring, selection operation and judgment on whether the termination condition is met. The method is reasonable in design, simple and convenient in operation, convenient to realize and good in use effect and high in practical value; the classification precision of the obtained disaggregated model is high, the training speed is high and the number of support vectors is less.

Description

technical field [0001] The invention relates to a method for optimizing parameters of an SVM classifier model, in particular to a method for optimizing parameters of a binary classification model by using a genetic algorithm. Background technique [0002] Support Vector Machine (SVM) is a new pattern recognition method in the 1990s, which maps the input space to a high-dimensional space through nonlinear transformation, and obtains the optimal classification hyperplane in the new space. Support vector machine classification is to separate the two types of sample points in the training samples by looking for a classification hyperplane, and to maximize the interval between the classification hyperplanes to achieve linearly separable optimal classification. For the case of linear inseparability, the kernel function is used to map the data of the low-dimensional input space to the high-dimensional space, so that the linear inseparable problem of the low-dimensional space is tra...

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Problems solved by technology

[0003] In fact, in the support vector machine classification algorithm, when the selected kernel function is the RBF kernel function, the penalty parameter C and the kernel parameter γ of the selected radial basis function are key parameters, which have a great impact on the performance of SVM classification

At present, when determining the penalty parameter C and the kernel parameter γ, it is mostly determined by a large number

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