{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T15:15:08Z","timestamp":1726067708793},"publisher-location":"Singapore","reference-count":34,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811533402"},{"type":"electronic","value":"9789811533419"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-981-15-3341-9_20","type":"book-chapter","created":{"date-parts":[[2020,2,15]],"date-time":"2020-02-15T07:02:24Z","timestamp":1581750144000},"page":"235-245","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Academic Achievement Prediction Model Enhanced by Stacking Network"],"prefix":"10.1007","author":[{"given":"Shaofeng","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingtao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,2,16]]},"reference":[{"key":"20_CR1","unstructured":"Ke, G., Meng, Q., Finley, T., et al.: Lightgbm: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems, pp. 3146\u20133154 (2017)"},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794. ACM (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"20_CR3","first-page":"85","volume":"2006","author":"MA Lemley","year":"1991","unstructured":"Lemley, M.A., Shapiro, C.: Patent holdup and royalty stacking. Tex. L. Rev. 2006, 85 (1991)","journal-title":"Tex. L. Rev."},{"issue":"2","key":"20_CR4","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123\u2013140 (1996)","journal-title":"Mach. Learn."},{"key":"20_CR5","volume-title":"The Way We Think: Conceptual Blending and the Mind\u2019s Hidden Complexities","author":"G Fauconnier","year":"2008","unstructured":"Fauconnier, G., Turner, M.: The Way We Think: Conceptual Blending and the Mind\u2019s Hidden Complexities. Basic Books, New York (2008)"},{"issue":"1","key":"20_CR6","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1109\/34.655647","volume":"20","author":"HA Rowley","year":"1998","unstructured":"Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 23\u201338 (1998)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"20_CR7","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1109\/72.97934","volume":"2","author":"DF Specht","year":"1991","unstructured":"Specht, D.F.: A general regression neural network. IEEE Trans. Neural Netw. 2(6), 568\u2013576 (1991)","journal-title":"IEEE Trans. Neural Netw."},{"key":"20_CR8","unstructured":"Krogh, A., Vedelsby, J.: Neural network ensembles, cross validation, and active learning. In: Advances in Neural Information Processing Systems, pp. 231\u2013238 (1995)"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Li, J., Chang, H., Yang, J.: Sparse deep stacking network for image classification. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)","DOI":"10.1609\/aaai.v29i1.9786"},{"key":"20_CR10","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., et al.: CatBoost: unbiased boosting with categorical features. In: Advances in Neural Information Processing Systems, pp. 6638\u20136648 (2018)"},{"key":"20_CR11","doi-asserted-by":"crossref","unstructured":"Odom, M.D., Sharda, R.: A neural network model for bankruptcy prediction. In: 1990 IJCNN International Joint Conference on Neural Networks, pp. 163\u2013168. IEEE (1990)","DOI":"10.1109\/IJCNN.1990.137710"},{"issue":"5","key":"20_CR12","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1093\/aje\/kws241","volume":"177","author":"S Rose","year":"2013","unstructured":"Rose, S.: Mortality risk score prediction in an elderly population using machine learning. Am. J. Epidemiol. 177(5), 443\u2013452 (2013)","journal-title":"Am. J. Epidemiol."},{"key":"20_CR13","first-page":"101","volume":"4","author":"J Grady","year":"1999","unstructured":"Grady, J., Oakley, T., Coulson, S.: Blending and metaphor. Amst. Stud. Theory Hist. Linguist. Sci. Ser. 4, 101\u2013124 (1999)","journal-title":"Amst. Stud. Theory Hist. Linguist. Sci. Ser."},{"issue":"Nov","key":"20_CR14","first-page":"933","volume":"4","author":"Y Freund","year":"2003","unstructured":"Freund, Y., Iyer, R., Schapire, R.E., et al.: An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 4(Nov), 933\u2013969 (2003)","journal-title":"J. Mach. Learn. Res."},{"key":"20_CR15","unstructured":"Schapire, R.E.: A brief introduction to boosting. In: IJCAI, vol. 99, pp. 1401\u20131406 (1999)"},{"key":"20_CR16","unstructured":"Solomatine, D.P., Shrestha, D.L.: AdaBoost. RT: a boosting algorithm for regression problems. In: 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541), vol. 2, pp. 1163\u20131168. IEEE (2004)"},{"key":"20_CR17","unstructured":"Kudo, T., Matsumoto, Y.: A boosting algorithm for classification of semi-structured text. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 301\u2013308 (2004)"},{"key":"20_CR18","unstructured":"Yosinski, J., Clune, J., Bengio, Y., et al.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320\u20133328 (2014)"},{"issue":"7639","key":"20_CR19","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A., Kuprel, B., Novoa, R.A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115 (2017)","journal-title":"Nature"},{"key":"20_CR20","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256 (2010)"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Hecht-Nielsen, R.: Theory of the backpropagation neural network. In: Neural Networks for Perception, pp. 65\u201393. Academic Press (1992)","DOI":"10.1016\/B978-0-12-741252-8.50010-8"},{"key":"20_CR22","unstructured":"Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of ICML, vol. 30, no. 1, p. 3 (2013)"},{"issue":"2","key":"20_CR23","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/37.1868","volume":"8","author":"D Psaltis","year":"1988","unstructured":"Psaltis, D., Sideris, A., Yamamura, A.A.: A multilayered neural network controller. IEEE Control Syst. Mag. 8(2), 17\u201321 (1988)","journal-title":"IEEE Control Syst. Mag."},{"key":"20_CR24","doi-asserted-by":"crossref","unstructured":"Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188 (2014)","DOI":"10.3115\/v1\/P14-1062"},{"issue":"5","key":"20_CR25","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1212\/WNL.0b013e31829d874e","volume":"81","author":"G Saposnik","year":"2013","unstructured":"Saposnik, G., Cote, R., Mamdani, M., et al.: JURaSSiC: accuracy of clinician vs risk score prediction of ischemic stroke outcomes. Neurology 81(5), 448\u2013455 (2013)","journal-title":"Neurology"},{"issue":"1","key":"20_CR26","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/BF02296657","volume":"68","author":"PW Holland","year":"2003","unstructured":"Holland, P.W., Hoskens, M.: Classical test theory as a first-order item response theory: application to true-score prediction from a possibly nonparallel test. Psychometrika 68(1), 123\u2013149 (2003)","journal-title":"Psychometrika"},{"key":"20_CR27","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/11731139_15","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"Y Liu","year":"2006","unstructured":"Liu, Y., An, A., Huang, X.: Boosting prediction accuracy on imbalanced datasets with SVM ensembles. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 107\u2013118. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11731139_15"},{"key":"20_CR28","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/978-3-540-39804-2_12","volume-title":"Knowledge Discovery in Databases: PKDD 2003","author":"NV Chawla","year":"2003","unstructured":"Chawla, N.V., Lazarevic, A., Hall, L.O., Bowyer, K.W.: SMOTEBoost: improving prediction of the minority class in boosting. In: Lavra\u010d, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 107\u2013119. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/978-3-540-39804-2_12"},{"issue":"4","key":"20_CR29","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1214\/07-STS242","volume":"22","author":"P B\u00fchlmann","year":"2007","unstructured":"B\u00fchlmann, P., Hothorn, T.: Boosting algorithms: regularization, prediction and model fitting. Stat. Sci. 22(4), 477\u2013505 (2007)","journal-title":"Stat. Sci."},{"key":"20_CR30","doi-asserted-by":"crossref","unstructured":"Bagnell, J.A., Chestnutt, J., Bradley, D.M., et al.: Boosting structured prediction for imitation learning. In: Advances in Neural Information Processing Systems, pp. 1153\u20131160 (2007)","DOI":"10.7551\/mitpress\/7503.003.0149"},{"issue":"6","key":"20_CR31","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1021\/acs.jcim.7b00028","volume":"57","author":"X Du","year":"2017","unstructured":"Du, X., Sun, S., Hu, C., et al.: DeepPPI: boosting prediction of protein-protein interactions with deep neural networks. J. Chem. Inf. Model. 57(6), 1499\u20131510 (2017)","journal-title":"J. Chem. Inf. Model."},{"issue":"2","key":"20_CR32","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1109\/TII.2012.2224355","volume":"10","author":"N Lu","year":"2012","unstructured":"Lu, N., Lin, H., Lu, J., et al.: A customer churn prediction model in telecom industry using boosting. IEEE Trans. Industr. Inf. 10(2), 1659\u20131665 (2012)","journal-title":"IEEE Trans. Industr. Inf."},{"issue":"2","key":"20_CR33","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s11222-009-9148-5","volume":"20","author":"P B\u00fchlmann","year":"2010","unstructured":"B\u00fchlmann, P., Hothorn, T.: Twin boosting: improved feature selection and prediction. Stat. Comput. 20(2), 119\u2013138 (2010)","journal-title":"Stat. Comput."},{"issue":"4","key":"20_CR34","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","volume":"38","author":"JH Friedman","year":"2002","unstructured":"Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38(4), 367\u2013378 (2002)","journal-title":"Comput. Stat. Data Anal."}],"container-title":["Communications in Computer and Information Science","Digital TV and Wireless Multimedia Communication"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-3341-9_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T19:14:56Z","timestamp":1695755696000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-15-3341-9_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789811533402","9789811533419"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-3341-9_20","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"16 February 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IFTC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Forum on Digital TV and Wireless Multimedia Communications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"19 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":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iftc2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.siga.com.cn\/iftc2019.html","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"120","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":"34","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":"28% - 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","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":"-","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)"}}]}}