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《工程(英文)》 >> 2019年 第5卷 第1期 doi: 10.1016/j.eng.2018.11.018

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

a College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
b School of Public Policy and Management, Tsinghua University, Beijing 100084, China
c School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
d Center for Strategic Studies, Chinese Academy of Engineering, Beijing 100088, China

收稿日期: 2018-03-05 修回日期: 2018-06-02 录用日期: 2018-11-07 发布日期: 2019-01-11

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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。

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参考文献

[ 1 ] Crevier D. AI: the tumultuous history of the search for artificial intelligence. New York: Basic Books, Inc.; 1993. 链接1

[ 2 ] Pan Y. Heading toward Artificial Intelligence 2.0. Engineering 2016;2 (4):409–13. 链接1

[ 3 ] State Council of the People’s Republic of China. Development Plan for a NextGeneration Artificial Intelligence [Internet]. Beijing: www.gov.cn. [cited 2018 Mar 5]. Available from: http://english.gov.cn/policies/latest_releases/2017/07/ 20/content_281475742458322.htm.

[ 4 ] State Council Information Office of the People’s Republic of China. The policy interpretation of Development Planning for a Next-Generation Artificial Intelligence [Internet]. Beijing: www.scio.gov.cn. [cited 2018 Mar 5]. Available from: http://www.scio.gov.cn/34473/34515/Document/1559231/ 1559231.htm. Chinese.

[ 5 ] Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science 2006;313(5786):504–7. 链接1

[ 6 ] Zhuang Y, Chen C, Pan Y. Challenges and opportunities: from big data to knowledge in AI 2.0. Front Inf Technol Electronic Eng 2017;18(1):3–14. 链接1

[ 7 ] Al-Qizwini M, Barjasteh I, Al-Qassab H, Radha H. Deep learning algorithm for autonomous driving using GoogLeNet. In: Proceedings of the 2017 IEEE Intelligent Vehicles Symposium; 2017 Jun 11–14; Los Angeles, CA, USA. New York: IEEE; 2017. p. 89–96. 链接1

[ 8 ] Wang L, Sng D. Deep learning algorithms with applications to video analytics for a smart city: a survey. 2016: arXiv:1511.06434.

[ 9 ] Mohamed A, Dahl G, Hinton G. Acoustic modeling using deep belief networks. IEEE Trans Audio Speech Lang Process 2012;20(1):14–22. 链接1

[10] Jones N. Artificial-intelligence institute launches free science search engine [Internet]. Heidelberg: Springer Nature. c2018 [cited 2018 Mar 5]. Available from: https://www.nature.com/news/artificial-intelligence-institute-launchesfree-science-search-engine-1.18703.

[11] Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge: The MIT Press; 2016. 链接1

[12] Zhuang F, Luo P, Qing H, Shi Z. Survey on transfer learning research. J Software 2015;26:26–39. Chinese. 链接1

[13] Chen J, Yang J, Zhou H, Xiang H, Zhu Z, Li Y, et al. CPS modeling of CNC machine tool work processes using an instruction-domain based approach. Engineering 2015;1(2):247–60. 链接1

[14] Urban F, Zhou Y, Nordensvard J, Narain A. Firm-level technology transfer and technology cooperation for wind energy between Europe, China and India: from north–south to south–north cooperation? Energy Sustainable Dev 2015;28:29–40. 链接1

[15] Zhou Y, Zhang H, Ding M, Su J. How public demonstration project affects the emergence of a new industry: an empirical study on electric vehicle demonstration project in China. In: Proceedings of the 2013 Suzhou Silicon Valley–Beijing International Innovation Conference; 2013 Jul 8–9. New York: IEEE; 2013. p. 234–9. 链接1

[16] Zhou Y, Minshall T. Building global products and competing in innovation: the role of Chinese university spin–outs and required innovation capabilities. Int J Technol Manage 2014;64(2):180–209. 链接1

[17] Xu G, Wu Y, Minshall T, Zhou Y. Exploring innovation ecosystems across science, technology, and business: a case of 3D printing in China. Technol Forecast Social Change 2017;136:180–221. 链接1

[18] Li X, Zhou Y, Xue L, Huang L. Roadmapping for industrial emergence and innovation gaps to catch-up: a patent analysis of OLED industry in China. Int J Technol Manage 2016;7(1–3):105–43. 链接1

[19] Li X, Zhou Y, Xue L, Huang L. Integrating bibliometrics and roadmapping methods: a case of dye-sensitized solar cell technology-based industry in China. Technol Forecast Social Change 2015;97:205–22. 链接1

[20] Zhou Y, Pan M, Urban F. Comparing the international knowledge flow of China’s wind and solar photovoltaic (PV) industries: patent analysis and implications for sustainable development. Sustainability 2018;10(6):1883. 链接1

[21] Theodoridis S, Koutroumbas K. Pattern recognition. 3rd ed. Orlando: Academic Press; 2006. 链接1

[22] Nordensvard J, Zhou Y, Zhang X. Innovation core, innovation semi-periphery and technology transfer: the case of wind energy patents. Energy Policy 2018;120:213–27. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[27] DeRouin E, Brown J. Neural network training on unequally represented classes. New York: ASME Press; 1991. 链接1

[28] Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 2002;16(1):321–57. 链接1

[29] Han H, Wang WY, Mao BH. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Huang DS, Zhang XP, Huang GB, editors. Advances in intelligent computing. Berlin: Springer; 2005. p. 878–87. 链接1

[30] He H, Bai Y, Garcia EA, Shutao L. ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: Proceedings of the 2008 IEEE International Joint Conference on Neural Networks; 2008 Jun 1–8; Hong Kong, China. New York: IEEE; 2008. p. 1322–8. 链接1

[31] Barua S, Islam MM, Yao X, Kazuyuki M. MWMOTE–majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans Knowl Data Eng 2014;26(2):405–25. 链接1

[32] Xie Z, Jiang L, Ye T, Li X. A synthetic minority oversampling method based on local densities in low-dimensional space for imbalanced learning. In: Renz M, Shahabi C, Zhou X, Cheema M, editors. Database systems for advanced applications. Cham: Springer; 2015. p. 3–18. 链接1

[33] Douzas G, Bacao F. Self-Organizing Map Oversampling (SOMO) for imbalanced data set learning. Expert Syst Appl 2017;82:40–52. 链接1

[34] Bishop CM. Training with noise is equivalent to Tiknonov regularization. Neural Comput 1995;7(1):108–16. 链接1

[35] Zhou Z, Jiang Y. Nec4.5: neural ensemble based C4.5. IEEE Trans Knowl Data Eng 2004;16(6):770–3. 链接1

[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. 链接1

[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. 链接1

[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. 链接1

[39] Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KG, editors. Advances in neural information processing systems. La Jolla: Neural Information Processing Systems Foundation, Inc.; 2014. p. 2672–80. 链接1

[40] Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA. Generative adversarial networks: an overview. IEEE Signal Process Mag 2018;35(1):53–65. 链接1

[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.

[43] Gou C, Wu Y, Wang K, Wang F, Ji Q. Learning-by-synthesis for accurate eye detection. In: Proceedings of the 23rd International Conference on Pattern Recognition; 2016 Dec 4–8; Cancun, Mexico. New York: IEEE; 2017. p. 3362–7. 链接1

[44] Li J, Monroe W, Shi T, Jean S, Ritter A, Jurafsky D. Adversarial learning for neural dialogue generation. 2017:arXiv:1701.06547.

[45] Pascual S, Bonafonte A, Serrà J. SEGAN: speech enhancement generative adversarial network. 2017:arXiv:1703.09452.

[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. 链接1

[47] Douzas G, Bacao F. Effective data generation for imbalanced learning using conditional generative adversarial networks. Expert Syst Appl 2017;91:464–71. 链接1

[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.

[50] Ratliff LJ, Burden SA, Sastry SS. Characterization and computation of local Nash equilibria in continuous games. In: Proceedings of the 51st Annual Allerton Conference on Communication, Control, and Computing; 2013 Oct 2–4; Monticello, IL, USA. New York: IEEE; 2006. p. 917–24. 链接1

[51] Danihelka I, Lakshminarayanan B, Uria B, Wierstra D, Dayan P. Comparison of maximum likelihood and GAN-based training of real NVPs. 2017: arXiv:1705.05263.

[52] Yang Q, Yan P, Zhang Y, Yu H, Shi Y, Mou X, et al. Low dose CT Image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans Med Imaging 2017;37(6):1348–57. 链接1

[53] Mcdaniel P, Papernot N, Celik ZB. Machine learning in adversarial settings. IEEE Secur Privacy 2016;14(3):68–72. 链接1

[54] Sousa LR, Miranda T, Sousa RL, Tinoco J. The use of data mining techniques in rockburst risk assessment. Engineering 2017;3(4):552–8. 链接1

[55] Sun Y, Kamel MS, Wong AKC, Wang Y. Cost-sensitive boosting for classification of imbalanced data. Pattern Recognit 2007;40(12):3358–78. 链接1

[56] Farazi AP, DePinho RA. Hepatocellular carcinoma pathogenesis: from genes to environment. Nat Rev Cancer 2006;6(9):674–87. 链接1

[57] Arzumanyan A, Reis HMGPV, Feitelson MA. Pathogenic mechanisms in HBVand HCV-associated hepatocellular carcinoma. Nat Rev Cancer 2013;13 (2):123–35. 链接1

[58] Mechref Y, Hu Y, Garcia A, Zhou S, Desantos-Garcia JL, Hussein A. Defining putative glycan cancer biomarkers by MS. Bioanalysis 2012;4(20):2457–69. 链接1

[59] Tang Z, Varghese RS, Bekesova S, Loffredo CA, Hamid MA, Kyselova Z, et al. Identification of N-glycan serum markers associated with hepatocellular carcinoma from mass spectrometry data. J Proteome Res 2010;9(1):104–12. 链接1

[60] Kronewitter SR, De Leoz MLA, Strum JS, An HJ, Dimapasoc LM, Guerrero A, et al. The glycolyzer: automated glycan annotation software for high performance mass spectrometry and its application to ovarian cancer glycan biomarker discovery. Proteomics 2012;12(15–16):2523–38. 链接1

[61] Pierce M, Buckhaults P, Chen L, Fregien N. Regulation of Nacetylglucosaminyltransferase V and Asn-linked oligosaccharide b(1,6) branching by a growth factor signaling pathway and effects on cell adhesion and metastatic potential. Glycoconjugate J 1997;14(5):623–30. 链接1

[62] Lau KS, Dennis JW. N-Glycans in cancer progression. Glycobiology 2008;18 (10):750–60. 链接1

[63] Saldova R, Royle L, Radcliffe CM, Abd Hamid UM, Evans R, Arnold JN, et al. Ovarian cancer is associated with changes in glycosylation in both acute-phase proteins and IgG. Glycobiology 2007;17(12):1344–56. 链接1

[64] Noda K, Miyoshi E, Gu J, Gao CX, Nakahara S, Kitada T, et al. Relationship between elevated FX expression and increased production of GDP-L-fucose, a common donor substrate for fucosylation in human hepatocellular carcinoma and hepatoma cell lines. Cancer Res 2003;63(19):6282–9. 链接1

[65] Basu PS, Majhi R, Batabyal SK. Lectin and serum-PSA interaction as a screening test for prostate cancer. Clin Biochem 2003;36(5):373–6. 链接1

[66] Arnold JN, Saldova R, Hamid UMA, Rudd PM. Evaluation of the serum N-linked glycome for the diagnosis of cancer and chronic inflammation. Proteomics 2008;8(16):3284–93. 链接1

[67] Adamczyk B, Tharmalingam T, Rudd PM. Glycans as cancer biomarkers. Biochim Biophys Acta Gen Subj 2012;1820(9):1347–53. 链接1

[68] Deguchi K, Keira T, Yamada K, Ito H, Takegawa Y, Nakagawa H, et al. Twodimensional hydrophilic interaction chromatography coupling anionexchange and hydrophilic interaction columns for separation of 2- pyridylamino derivatives of neutral and sialylated N-glycans. J Chromatography A 2008;1189(1–2):169–74. 链接1

[69] Siemerink E, Mulder NH, Brouwers AH, Hospers GA. Early prediction of response to sorafenib treatment in patients with hepatocellular carcinoma (HCC) with 18F-fluorodeoxyglucose-positron emission tomography (18F-FDGPET). J Clin Oncol 2008;26(21):1–15. 链接1

[70] Holzinger A, Malle B, Kieseberg P, Roth PM, Müller H, Reihs R. Machine learning and knowledge extraction in digital pathology needs an integrative approach lecture notes in computer science. In: Holzinger A, Goebel R, Ferri M, Palade V, editors. Towards integrative machine learning and knowledge extraction. Cham: Springer; 2017. 链接1

[71] Breiman L. Random forests. Mach Learn 2001;45(1):5–32. 链接1

[72] Mitchell T. Machine learning. Zeng H, Zhang Y, translator. Beijing: China Machine Press; 2003. Chinese. 链接1

[73] Liu P, Zhou Y, Zhou DK, Xue L. Energy Performance Contract models for the diffusion of green-manufacturing technologies in China: a stakeholder analysis from SMEs’ perspective. Energy Policy 2017;106:59–67. 链接1

[74] Kong D, Feng Q, Zhou Y, Xue L. Local implementation for green-manufacturing technology diffusion policy in China: from the user firms’ perspectives. J Cleaner Prod 2016;129:113–24. 链接1

[75] Zhou Y, Xu G, Minshall T, Liu P. How do public demonstration projects promote green-manufacturing technologies? A case study from China. Sustainable Dev 2015;23(4):217–31. 链接1

[76] Kong D, Zhou Y, Liu Y, Xue L. Using the data mining method to assess the innovation gap: a case of industrial robotics in a catching-up country. Technol Forecasting Social Change 2017;119:80–97. 链接1

[77] Li M, Zhou Y. Visualizing the knowledge profile on self-powered technology. Nano Energy 2018;51:250–9. 链接1

[78] Wang B, Liu Y, Zhou Y, Wen Z. Emerging nanogenerator technology in China: a review and forecast using integrating bibliometrics, patent analy and technology roadmapping methods. Nano Energy 2018;46:322–30. 链接1

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