Generative adversarial network oversampling method and device based on simulated annealing genetic algorithm

Abstract

The invention provides a generative adversarial network oversampling method and device based on a simulated annealing genetic algorithm, and the method comprises the steps: determining the corresponding relation between sample data and optimal filial generation sample data through the adversarial learning capability of a generative adversarial artificial neural network; specifically, determining the optimal filial generation sample data according to a preset individual fitness condition; determining network parameters of a generative adversarial artificial neural network according to the optimal filial generation sample data; determining the corresponding relation according to the network parameters; acquiring target sample data; and determining optimal filial generation target sample datacorresponding to the target sample data through the corresponding relationship. A plurality of adversarial learning targets are used simultaneously to train a generative network, so the limitation ofa single adversarial learning target is overcome; whether the generative network is updated or not is selected by using a simulated annealing algorithm, so the model is prevented from falling into alocal optimal solution, and the model is converged to global optimum.

Description

technical field [0001] The present application relates to the field of medical detection, in particular to an oversampling method and device for a generative adversarial network based on a simulated annealing genetic algorithm. Background technique [0002] The class imbalance problem exists widely in machine learning applications. The performance of many standard classifiers degrades significantly when the data used for classification suffers from class imbalance. This is because most classification algorithms assume a balanced distribution of training data, applying the same misclassification cost to data of different classes. When the data is imbalanced, the key to improving the data performance of the classifier is to accurately capture the distribution of the minority class data. However, learning an accurate distribution from very few examples of the minority class is difficult. [0003] A common method to solve the problem of class imbalance is to increase the samp...

AI Technical Summary

Problems solved by technology

Although GANs have been successfully applied to many tasks, there are still some problems that hinder the good training of GANs, such as unstable training, crash mode and hyperparameter tuning

At the same time, due to the limited number of samples in the minority class, GAN may only be able to learn a part of the distribution of the minority class at the end of training, so it may fall into a local optimum

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