Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology
Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology
复制标题
基于Wasserstein GAN的新一代人工智能小样本增强：生物学癌症分期数据的案例研究
DOI：
10.1016/j.eng.2018.11.018
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发表时间：
2019-02-01
期刊：
影响因子：
12.8
通讯作者：
Wang, Zihong
中科院分区：
文献类型：
--
作者：
Liu, Yufei;Zhou, Yuan;Wang, Zihong
利用基于大数据的深度学习算法实现新一代人工智能至关重要。深度学习的有效利用很大程度上依赖于标记样本的数量，这限制了深度学习在小样本环境中的应用。在本文中，我们提出了一种基于生成对抗网络（GAN）和深度神经网络（DNN）相结合的方法。首先，将原始样本分为训练集和测试集。利用训练集对GAN进行训练，生成合成样本数据，扩大了训练集。然后，用合成样本训练DNN分类器。最后，用测试集对分类器进行了测试，并通过指标验证了该方法在小样本量下进行多分类的有效性。作为一个经验案例，该方法随后被应用于用一个小的标记样本量来确定癌症的阶段。实验结果表明，该方法比传统方法具有更高的精度。本研究尝试将经典的基于原始样本的统计机器学习分类方法转化为基于数据增强的深度学习分类方法。这种方法的使用将有助于扩展基于深度学习的新一代人工智能的应用场景，并提高应用效果。该研究也有望为新一代人工智能的全面推广做出贡献。(c) 2019年提交人。由爱思唯尔有限公司代中国工程院高等教育出版社有限公司出版。
It is essential to utilize deep-learning algorithms based on big data for the implementation of the new generation of artificial intelligence. Effective utilization of deep learning relies considerably on the number of labeled samples, which restricts the application of deep learning in an environment with a small sample size. In this paper, we propose an approach based on a generative adversarial network (GAN) combined with a deep neural network (DNN). First, the original samples were divided into a training set and a test set. The GAN was trained with the training set to generate synthetic sample data, which enlarged the training set. Next, the DNN classifier was trained with the synthetic samples. Finally, the classifier was tested with the test set, and the effectiveness of the approach for multi-classification with a small sample size was validated by the indicators. As an empirical case, the approach was then applied to identify the stages of cancers with a small labeled sample size. The experimental results verified that the proposed approach achieved a greater accuracy than traditional methods. This research was an attempt to transform the classical statistical machine-learning classification method based on original samples into a deep-learning classification method based on data augmentation. The use of this approach will contribute to an expansion of application scenarios for the new generation of artificial intelligence based on deep learning, and to an increase in application effectiveness. This research is also expected to contribute to the comprehensive promotion of new-generation artificial intelligence. (C) 2019 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company.