《Table 3Performance of different classification strategies.》

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《基于Wasserstein GAN的新一代人工智能小样本数据增强方法——以生物领域癌症分期数据为例》

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The RF and NB models trained with 4000 synthetic samples generated by WGAN exhibited different performance changes.The RF model had worse indexes than the model trained with the oversampling samples，while the NB model’s indexes increased considerably.These results imply that with the synthetic samples generated by WGAN，the classical machine-learning models did not provide good results.In the proposed framework，the deeplearning model DNN with synthetic samples from WGAN performed best among all the considered classifiers.Compared with the oversampling samples generated by SMOTE，this method further increased the accuracy from 64.52%to 70.97%，the F-measure value from 66.05%to 70.07%，and the G-mean value from63.32%to 68.39%，which demonstrates that WGAN with a DNN effectively solved the HCC stage recognition problem.Above all，these findings indicate that the deep-learning method can successfully be applied to a multi-classification problem with a limited number of samples.