{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T08:27:45Z","timestamp":1759134465191,"version":"3.40.4"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030461324","type":"print"},{"value":"9783030461331","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-46133-1_37","type":"book-chapter","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T22:02:59Z","timestamp":1745964179000},"page":"621-637","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Generative Adversarial Networks for Failure Prediction"],"prefix":"10.1007","author":[{"given":"Shuai","family":"Zheng","sequence":"first","affiliation":[]},{"given":"Ahmed","family":"Farahat","sequence":"additional","affiliation":[]},{"given":"Chetan","family":"Gupta","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,30]]},"reference":[{"key":"37_CR1","unstructured":"Monaghan, A.: Hotpoint tells customers to check fridge-freezers after grenfell tower fire. The Guardian (2017)"},{"issue":"1","key":"37_CR2","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1109\/TCAPT.2006.870387","volume":"29","author":"NM Vichare","year":"2006","unstructured":"Vichare, N.M., Pecht, M.G.: Prognostics and health management of electronics. IEEE Trans. Compon. Packag. Technol. 29(1), 222\u2013229 (2006)","journal-title":"IEEE Trans. Compon. Packag. Technol."},{"issue":"5","key":"37_CR3","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1007\/s10845-014-0933-4","volume":"27","author":"A Mosallam","year":"2016","unstructured":"Mosallam, A., Medjaher, K., Zerhouni, N.: Data-driven prognostic method based on bayesian approaches for direct remaining useful life prediction. J. Intell. Manuf. 27(5), 1037\u20131048 (2016)","journal-title":"J. Intell. Manuf."},{"key":"37_CR4","doi-asserted-by":"crossref","unstructured":"Pecht, M.G.: A prognostics and health management roadmap for information and electronics-rich systems. IEICE ESS Fundam. Rev. 3(4), $$4\\_{25}$$\u2013$$4\\_{32}$$ (2010)","DOI":"10.1587\/essfr.3.4_25"},{"key":"37_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/978-3-642-23400-2_19","volume-title":"Euro-Par 2011 Parallel Processing","author":"S Zheng","year":"2011","unstructured":"Zheng, S., Shae, Z.-Y., Zhang, X., Jamjoom, H., Fong, L.: Analysis and modeling of social influence in high performance computing workloads. In: Jeannot, E., Namyst, R., Roman, J. (eds.) Euro-Par 2011. LNCS, vol. 6852, pp. 193\u2013204. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-23400-2_19"},{"key":"37_CR6","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"key":"37_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1007\/978-3-319-68612-7_71","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2017","author":"F Calimeri","year":"2017","unstructured":"Calimeri, F., Marzullo, A., Stamile, C., Terracina, G.: Biomedical data augmentation using generative adversarial neural networks. In: Lintas, A., Rovetta, S., Verschure, P.F.M.J., Villa, A.E.P. (eds.) ICANN 2017. LNCS, vol. 10614, pp. 626\u2013634. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68612-7_71"},{"key":"37_CR8","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., Zheng, Z., Li, S., Yang, Y.: Camera style adaptation for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5157\u20135166 (2018)","DOI":"10.1109\/CVPR.2018.00541"},{"issue":"9","key":"37_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2352\/ISSN.2470-1173.2018.09.SRV-127","volume":"2018","author":"K Yun","year":"2018","unstructured":"Yun, K., Bustos, J., Lu, T.: Predicting rapid fire growth (flashover) using conditional generative adversarial networks. Electron. Imaging 2018(9), 1\u20134 (2018)","journal-title":"Electron. Imaging"},{"key":"37_CR10","unstructured":"Dai, Z., Yang, Z., Yang, F., Cohen, W.W., Salakhutdinov, R.R.: Good semi-supervised learning that requires a bad GAN. In: Advances in Neural Information Processing Systems, pp. 6510\u20136520 (2017)"},{"key":"37_CR11","unstructured":"Tran, T., Pham, T., Carneiro, G., Palmer, L., Reid, I.: A Bayesian data augmentation approach for learning deep models. In: Advances in Neural Information Processing Systems, pp. 2797\u20132806 (2017)"},{"key":"37_CR12","doi-asserted-by":"crossref","unstructured":"Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., Krishnan, D.: Unsupervised pixel-level domain adaptation with generative adversarial networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, p. 7 (2017)","DOI":"10.1109\/CVPR.2017.18"},{"key":"37_CR13","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2172\u20132180 (2016)"},{"key":"37_CR14","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.neucom.2017.06.082","volume":"276","author":"S Nejatian","year":"2018","unstructured":"Nejatian, S., Parvin, H., Faraji, E.: Using sub-sampling and ensemble clustering techniques to improve performance of imbalanced classification. Neurocomputing 276, 55\u201366 (2018)","journal-title":"Neurocomputing"},{"key":"37_CR15","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"37_CR16","doi-asserted-by":"crossref","unstructured":"He, H., Bai, Y., Garcia, E.A., Li, S.: ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322\u20131328. IEEE (2008)","DOI":"10.1109\/IJCNN.2008.4633969"},{"key":"37_CR17","doi-asserted-by":"crossref","unstructured":"Shen, W., Wang, X., Wang, Y., Bai, X., Zhang, Z.: DeepContour: a deep convolutional feature learned by positive-sharing loss for contour detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3982\u20133991 (2015)","DOI":"10.1109\/CVPR.2015.7299024"},{"key":"37_CR18","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1109\/TNNLS.2018.2832648","volume":"30","author":"C Zhang","year":"2018","unstructured":"Zhang, C., Tan, K.C., Li, H., Hong, G.S.: A cost-sensitive deep belief network for imbalanced classification. IEEE Trans. Neural Netw. Learn. Syst. 30, 109\u2013122 (2018)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"37_CR19","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1016\/j.ymssp.2018.03.025","volume":"110","author":"F Jia","year":"2018","unstructured":"Jia, F., Lei, Y., Lu, N., Xing, S.: Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization. Mech. Syst. Signal Process. 110, 349\u2013367 (2018)","journal-title":"Mech. Syst. Signal Process."},{"issue":"1","key":"37_CR20","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1109\/TSMCB.2008.2002909","volume":"39","author":"Y Tang","year":"2009","unstructured":"Tang, Y., Zhang, Y.Q., Chawla, N.V., Krasser, S.: SVMs modeling for highly imbalanced classification. IEEE Trans. Syst. Man Cybern. B (Cybern.) 39(1), 281\u2013288 (2009)","journal-title":"IEEE Trans. Syst. Man Cybern. B (Cybern.)"},{"key":"37_CR21","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1007\/978-3-662-44845-8_26","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"S Zheng","year":"2014","unstructured":"Zheng, S., Ding, C.: Kernel alignment inspired linear discriminant analysis. In: Calders, T., Esposito, F., H\u00fcllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8726, pp. 401\u2013416. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-662-44845-8_26"},{"key":"37_CR22","doi-asserted-by":"crossref","unstructured":"Zheng, S., Cai, X., Ding, C.H., Nie, F., Huang, H.: A closed form solution to multi-view low-rank regression. In: AAAI, pp. 1973\u20131979 (2015)","DOI":"10.1609\/aaai.v29i1.9461"},{"key":"37_CR23","doi-asserted-by":"crossref","unstructured":"Zheng, S., Nie, F., Ding, C., Huang, H.: A harmonic mean linear discriminant analysis for robust image classification. In: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 402\u2013409. IEEE (2016)","DOI":"10.1109\/ICTAI.2016.0068"},{"key":"37_CR24","unstructured":"Zheng, S.: Machine learning: several advances in linear discriminant analysis, multi-view regression and support vector machine. Ph.D. thesis, The University of Texas at Arlington (2017)"},{"key":"37_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2861858","author":"S Zheng","year":"2018","unstructured":"Zheng, S., Ding, C., Nie, F., Huang, H.: Harmonic mean linear discriminant analysis. IEEE Trans. Knowl. Data Eng. (2018). https:\/\/doi.org\/10.1109\/TKDE.2018.2861858","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"37_CR26","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.neucom.2019.02.001","volume":"338","author":"S Zheng","year":"2019","unstructured":"Zheng, S., Ding, C.: Sparse classification using group matching pursuit. Neurocomputing 338, 83\u201391 (2019). https:\/\/doi.org\/10.1016\/j.neucom.2019.02.001","journal-title":"Neurocomputing"},{"key":"37_CR27","unstructured":"Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)"},{"key":"37_CR28","unstructured":"Lee, M., Seok, J.: Controllable generative adversarial network. arXiv preprint arXiv:1708.00598 (2017)"},{"key":"37_CR29","unstructured":"Zheng, S., Ding, C., Nie, F.: Regularized singular value decomposition and application to recommender system. arXiv preprint arXiv:1804.05090 (2018)"},{"key":"37_CR30","unstructured":"Zheng, S., Ding, C.: Minimal support vector machine. arXiv preprint arXiv:1804.02370 (2018)"},{"key":"37_CR31","unstructured":"Dua, D., Karra Taniskidou, E.: UCI machine learning repository (2017). http:\/\/archive.ics.uci.edu\/ml"},{"key":"37_CR32","doi-asserted-by":"crossref","unstructured":"Saxena, A., Goebel, K., Simon, D., Eklund, N.: Damage propagation modeling for aircraft engine run-to-failure simulation. In: 2008 International Conference on Prognostics and Health Management, pp. 1\u20139. IEEE (2008)","DOI":"10.1109\/PHM.2008.4711414"},{"key":"37_CR33","doi-asserted-by":"crossref","unstructured":"Zheng, S., Ristovski, K., Farahat, A., Gupta, C.: Long short-term memory network for remaining useful life estimation. In: 2017 IEEE International Conference on Prognostics and Health Management (ICPHM), pp. 88\u201395. IEEE (2017)","DOI":"10.1109\/ICPHM.2017.7998311"},{"key":"37_CR34","volume-title":"Data Mining: Concepts and Techniques","author":"J Han","year":"2011","unstructured":"Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Waltham (2011)"},{"key":"37_CR35","doi-asserted-by":"crossref","unstructured":"Zheng, S., Vishnu, A., Ding, C.: Accelerating deep learning with shrinkage and recall. In: 2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS), pp. 963\u2013970. IEEE (2016)","DOI":"10.1109\/ICPADS.2016.0129"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-46133-1_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T22:03:29Z","timestamp":1745964209000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-46133-1_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030461324","9783030461331"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-46133-1_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"30 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"W\u00fcrzburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ecmlpkdd2019.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"733","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"130","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.04","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5.3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"ECML PKDD Workshops Information: single-blind review, submissions: 200, full papers accepted: 70, short papers accepted: 46","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}