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Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology

机译:基于Wasserstein GAN的新一代人工智能小样本增强:生物学中癌症分期数据的案例研究

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摘要

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.
机译:利用基于大数据的深度学习算法来实现新一代人工智能至关重要。深度学习的有效利用在很大程度上取决于标记样本的数量,这限制了深度学习在样本量较小的环境中的应用。在本文中,我们提出了一种基于生成对抗网络(GAN)结合深度神经网络(DNN)的方法。首先,将原始样本分为训练集和测试集。 GAN用训练集进行了训练,以生成综合样本数据,从而扩大了训练集。接下来,使用合成样本对DNN分类器进行训练。最后,使用测试集对分类器进行测试,并通过指标验证了以小样本量进行多分类的方法的有效性。作为一个经验案例,然后将该方法应用于以较小的标记样本量来识别癌症的阶段。实验结果证明,该方法比传统方法具有更高的准确性。这项研究试图将基于原始样本的经典统计机器学习分类方法转变为基于数据扩充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,并提高应用程序的有效性。这项研究也有望为新一代人工智能的全面推广做出贡献。

著录项

  • 来源
    《工程(英文)》 |2019年第001期|P.156-163|共8页
  • 作者单位

    [1]College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;

    [2]School of Public Policy and Management, Tsinghua University, Beijing 100084, China;

    [4]Center for Strategic Studies, Chinese Academy of Engineering, Beijing 100088, China;

    [2]School of Public Policy and Management, Tsinghua University, Beijing 100084, China;

    [1]College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;

    [2]School of Public Policy and Management, Tsinghua University, Beijing 100084, China;

    [1]College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;

    [3]School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 CHI
  • 中图分类 自然科学总论;
  • 关键词

    Artificial intelligence; Generative adversarial network; Deep neural network; Small sample size; Cancer;

    机译:人工智能;对抗网络;深度神经网络;样本量小;癌症;
  • 入库时间 2022-08-19 04:28:25
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