基于VAE-CGAN的牦牛等级评定算法

在牦牛高效养殖过程中, 牦牛等级评定是牦牛育种工作中的重要环节. 为了在牦牛等级评定研究中, 降低数据集分布不平衡对牦牛等级预测结果的影响, 提出一种基于改进条件生成对抗网络模型的牦牛等级评定模型VAE-CGAN. 首先, 为获取高质量生成样本, 模型通过引入变分自编码器取代条件生成对抗网络输入中的随机噪声, 降低了随机变量带来的不确定性. 此外, 模型将牦牛标签作为条件信息输入到生成对抗模型中来获取指定类别的生成样本, 生成样本及训练样本则会被用于训练深度神经网络分类器. 实验结果显示, 模型整体预测准确率达到了97.9%. 而且与生成对抗网络相比较, 在数量较少的特级牦牛等级预测上的精准率、召回率和F1值分别提升了16.7%、16.6%和19.4%. 实验结果表明该模型可以实现高精准度和低误分类率的牦牛等级分类.

Grade Evaluation Algorithm of Yak Based on VAE-CGAN

Yak grade evaluation is an important part of high-efficiency yak breeding. To reduce the influence of imbalanced data set distribution on the prediction results of yak grading in the research, this study proposes a yak grade evaluation model based on an improved conditional generative adversarial network model, called VAE-CGAN. Firstly, to obtain high-quality generated samples, the model reduces the uncertainty from random variables by introducing a variational autoencoder to replace the random noise in the input of the conditional generative adversarial network. In addition, the model inputs the yak label as conditional information into the generative adversarial model to obtain the generated samples of the specified category, and the generated samples and training samples are utilized to train the deep neural network classifier. The experimental results show that the overall prediction accuracy of the model has reached 97.9%. The Precision, Recall, and F1 value on the grade prediction of premium yak have increased by 16.7%, 16.6%, and 19.4% respectively compared with those of the generative adversarial network. The results indicate the model can achieve yak classification with high accuracy and low misclassification rate.