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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摘要
以大数据为基础的深度学习算法在推动新一代人工智能快速发展中意义重大。然而深度学习的有效利用对标注样本数量的高度依赖,使得深度学习在小样本数据环境下的应用受到制约。本研究提出了一种基于生成对抗网络(generative adversarial network,GAN)和深度神经网络(deep neural network,DNN)分类器的方法。首先,将原始样本划分为训练集样本和测试集样本,采用训练集样本训练GAN后生成模拟样本数据,扩增训练集样本规模;然后,使用模拟样本训练DNN分类器;最后,使用测试集样本测试分类器,并通过指标验证该方法在小样本多分类问题下的有效性。作为实证案例,将该方法应用于生物领域癌症分期识别,结果表明该方法比传统方法获得更高的识别准确率。同时,该方法是一次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增强的深度学习分类方法的尝试。本研究有助于探索以深度学习为代表的新一代人工智能技术在应用范围与应用效果方面的潜力。这将对各领域全面推进新一代人工智能的发展具有重要意义。
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.
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.
引文
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[3]State Council of the People’s Republic of China.Development Plan for a NextGeneration Artificial Intelligence[Internet].Beijing:www.gov.cn.[cited 2018Mar 5].Available from:http://english.gov.cn/policies/latest_releases/2017/07/20/content_281475742458322.htm.
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[23]Pan M,Zhou Y,Zhou DK.Comparing the innovation strategies of Chinese and European wind turbine firms through a patent lens.Environ Innovation Societal Transitions.Epub 2017 Dec 27.
[24]Zhou Y,Pan M,Zhou DK,Xue L.Stakeholder risk and trust perceptions in the diffusion of green manufacturing technologies:evidence from China.J Environ Dev 2017;27(1):46-73.
[25]Zhou Y,Li X,Lema R,Urban F.Comparing the knowledge bases of wind turbine firms in Asia and Europe:patent trajectories,networks,and globalisation.Sci Public Policy 2016;43(2):476-91.
[26]Chen L,Xu J,Zhou Y.Regulating the environmental behavior of manufacturing SMEs:interfirm alliance as a facilitator.J Cleaner Prod 2017;165:393-404.
[27]DeRouin E,Brown J.Neural network training on unequally represented classes.New York:ASME Press;1991.
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[33]Douzas G,Bacao F.Self-Organizing Map Oversampling(SOMO)for imbalanced data set learning.Expert Syst Appl 2017;82:40-52.
[34]Bishop CM.Training with noise is equivalent to Tiknonov regularization.Neural Comput 1995;7(1):108-16.
[35]Zhou Z,Jiang Y.Nec4.5:neural ensemble based C4.5.IEEE Trans Knowl Data Eng 2004;16(6):770-3.
[36]Li DC,Lin YS.Using virtual sample generation to build up management knowledge in the early manufacturing stages.Eur J Operat Res 2006;175(1):413-34.
[37]Li D,Fang Y.A non-linearly virtual sample generation technique using group discovery and parametric equations of hypersphere.Exp Syst Appl 2009;36(1):844-51.
[38]Wang K,Gou C,Duan Y,Lin Y,Zheng X,Wang F.Generative adversarial networks:introduction and outlook.IEEE/CAA J Autom Sin 2017;4(4):588-98.
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[41]Radford A,Metz L,Chintala S.Unsupervised representation learning with deep convolutional generative adversarial networks.2015:arXiv:1512.03131.
[42]Santana E,Hotz G.Learning a driving simulator.2016:arXiv:1608.01230.
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[46]Fiore U,Santis AD,Perla F,Zanetti P,Palmieri F.Using generative adversarial networks for improving classification effectiveness in credit card fraud detection.Inf Sci 2019;479:448-55.
[47]Douzas G,Bacao F.Effective data generation for imbalanced learning using conditional generative adversarial networks.Expert Syst Appl2017;91:464-71.
[48]Bloice MD,Stocker C,Holzinger A.Augmentor:an image augmentation library for machine learning.2017:arXiv:1708.04680
[49]Arjovsky M,Chintala S,Bottou L.Wasserstein GAN.2017:arXiv:1701.07875.
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