{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T22:43:09Z","timestamp":1771022589976,"version":"3.50.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030788100","type":"print"},{"value":"9783030788117","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-78811-7_29","type":"book-chapter","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T23:22:37Z","timestamp":1625613757000},"page":"300-312","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Classification of Imbalanced Fetal Health Data by PSO Based Ensemble Recursive Feature Elimination ANN"],"prefix":"10.1007","author":[{"given":"Jun","family":"Gao","sequence":"first","affiliation":[]},{"given":"Canpeng","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Xijie","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Kaishan","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Hong","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,7]]},"reference":[{"issue":"5","key":"29_CR1","first-page":"311","volume":"9","author":"DA de Campos","year":"2000","unstructured":"de Campos, D.A., et al.: Sisporto 2.0: a program for automated analysis of cardiotocograms. J. Maternaletal Med. 9(5), 311\u201318 (2000)","journal-title":"J. Maternaletal Med."},{"key":"29_CR2","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.ins.2016.09.038","volume":"384","author":"M Bach","year":"2016","unstructured":"Bach, M., Werner, A., Ywiec, J., Pluskiewicz, W.: The study of under- and over-sampling methods\u2019 utility in analysis of highly imbalanced data on osteoporosis. Inf. Sci. 384, 174\u2013190 (2016)","journal-title":"Inf. Sci."},{"issue":"9","key":"29_CR3","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.3390\/app8091521","volume":"8","author":"L Brezo\u010dnik","year":"2018","unstructured":"Brezo\u010dnik, L., Fister, I., Podgorelec, V.: Swarm intelligence algorithms for feature selection: a review. Appl. Sci. 8(9), 1521 (2018)","journal-title":"Appl. Sci."},{"issue":"2 Part 2","key":"29_CR4","doi-asserted-by":"publisher","first-page":"4035","DOI":"10.1016\/j.eswa.2008.03.007","volume":"36","author":"CL Chang","year":"2009","unstructured":"Chang, C.L., Chen, C.H.: Applying decision tree and neural network to increase quality of dermatologic diagnosis. Exp. Syst. Appl. 36(2 Part 2), 4035\u20134041 (2009)","journal-title":"Exp. Syst. Appl."},{"key":"29_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1007\/978-3-030-34482-5_7","volume-title":"Smart Health","author":"J Wei","year":"2019","unstructured":"Wei, J., et al.: Imbalanced cardiotocography multi-classification for antenatal fetal monitoring using weighted random forest. In: Chen, H., Zeng, D., Yan, X., Xing, C. (eds.) ICSH 2019. LNCS, vol. 11924, pp. 75\u201385. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-34482-5_7"},{"issue":"47","key":"29_CR6","doi-asserted-by":"publisher","first-page":"35147","DOI":"10.1007\/s11042-020-08853-2","volume":"79","author":"S Das","year":"2020","unstructured":"Das, S., Mukherjee, H., Obaidullah, S.M., Roy, K., Saha, C.K.: Ensemble based technique for the assessment of fetal health using cardiotocograph \u2013 a case study with standard feature reduction techniques. Multimedia Tools Appl. 79(47), 35147\u201335168 (2020). https:\/\/doi.org\/10.1007\/s11042-020-08853-2","journal-title":"Multimedia Tools Appl."},{"key":"29_CR7","first-page":"487179","volume":"2013","author":"Y Ersen","year":"2013","unstructured":"Ersen, Y., Kilik\u00e7ier, K.: Determination of fetal state from cardiotocogram using LS-SVM with particle swarm optimization and binary decision tree. Comput. Math. Meth. Med. 2013, 487179 (2013)","journal-title":"Comput. Math. Meth. Med."},{"issue":"1\u20133","key":"29_CR8","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1023\/A:1012487302797","volume":"46","author":"I Guyon","year":"2002","unstructured":"Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1\u20133), 389\u2013422 (2002)","journal-title":"Mach. Learn."},{"issue":"9","key":"29_CR9","doi-asserted-by":"publisher","first-page":"526","DOI":"10.4236\/jbise.2012.59065","volume":"05","author":"ML Huang","year":"2012","unstructured":"Huang, M.L., Hsu, Y.Y.: Fetal distress prediction using discriminant analysis, decision tree, and artificial neural network. J. Biomed. Sci. Eng. 05(9), 526\u2013533 (2012)","journal-title":"J. Biomed. Sci. Eng."},{"issue":"1","key":"29_CR10","doi-asserted-by":"publisher","first-page":"012101","DOI":"10.1088\/1757-899X\/745\/1\/012101","volume":"745","author":"N Kadhim","year":"2020","unstructured":"Kadhim, N., Abed, J.K.: Enhancing the prediction accuracy for cardiotocography (CTG) using firefly algorithm and Naive Bayesian classifier. IOP Conf. Ser. Mater. Sci. Eng. 745(1), 012101 (2020)","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"29_CR11","doi-asserted-by":"publisher","first-page":"100663","DOI":"10.1016\/j.swevo.2020.100663","volume":"54","author":"BH Nguyen","year":"2020","unstructured":"Nguyen, B.H., Xue, B., Zhang, M.: A survey on swarm intelligence approaches to feature selection in data mining. Swarm Evol. Comput. 54, 100663 (2020)","journal-title":"Swarm Evol. Comput."},{"issue":"2","key":"29_CR12","doi-asserted-by":"publisher","first-page":"9913","DOI":"10.1007\/s10916-012-9913-4","volume":"37","author":"H Ocak","year":"2013","unstructured":"Ocak, H.: A medical decision support system based on support vector machines and the genetic algorithm for the evaluation of fetal well-being. J. Med. Syst. 37(2), 9913 (2013)","journal-title":"J. Med. Syst."},{"issue":"2","key":"29_CR13","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1447-0756.1986.tb00194.x","volume":"12","author":"Y Ohno","year":"2010","unstructured":"Ohno, Y., et al.: Assessment of fetal heart rate variability with abdominal fetal electrocardiogram: changes during fetal breathing movement. Asia-Oceania J. Obstet. Gynaecol. 12(2), 301\u2013304 (2010)","journal-title":"Asia-Oceania J. Obstet. Gynaecol."},{"key":"29_CR14","unstructured":"Rana, R., Pruthi, J.: Naive Bayes classification (2014)"},{"key":"29_CR15","doi-asserted-by":"publisher","first-page":"101903","DOI":"10.1016\/j.bspc.2020.101903","volume":"59","author":"B Richhariya","year":"2020","unstructured":"Richhariya, B., Tanveer, M., Rashid, A.H.: Diagnosis of Alzheimer\u2019s disease using universum support vector machine based recursive feature elimination (USVM-RFE). Biomed. Sig. Process. Control 59, 101903 (2020)","journal-title":"Biomed. Sig. Process. Control"},{"issue":"C","key":"29_CR16","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.asoc.2015.04.038","volume":"33","author":"H Sahin","year":"2015","unstructured":"Sahin, H., Subasi, A.: Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques. Appl. Soft Comput. 33(C), 231\u2013238 (2015)","journal-title":"Appl. Soft Comput."},{"issue":"4","key":"29_CR17","first-page":"1","volume":"1","author":"C Saunders","year":"2002","unstructured":"Saunders, C., et al.: Support vector machine. Comput. Sci. 1(4), 1\u201328 (2002)","journal-title":"Comput. Sci."},{"issue":"2","key":"29_CR18","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.compbiomed.2006.01.007","volume":"37","author":"S Shah","year":"2007","unstructured":"Shah, S., Kusiak, A.: Cancer gene search with data-mining and genetic algorithms. Comput. Biol. Med. 37(2), 251\u2013261 (2007)","journal-title":"Comput. Biol. Med."},{"issue":"3","key":"29_CR19","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1109\/TBME.2003.808824","volume":"50","author":"MG Signorini","year":"2003","unstructured":"Signorini, M.G., Magenes, G., Cerutti, S., Arduini, D.: Linear and nonlinear parameters for the analysis of fetal heart rate signal from cardiotocographic recordings. IEEE Trans. Biomed. Eng. 50(3), 365\u2013374 (2003)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"2","key":"29_CR20","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1097\/GCO.0b013e3282f73242","volume":"20","author":"JF Smith Jr","year":"2008","unstructured":"Smith Jr., J.F.: Fetal health assessment using prenatal diagnostic techniques. Curr. Opin. Obstet. Gynecol. 20(2), 152\u2013156 (2008)","journal-title":"Curr. Opin. Obstet. Gynecol."},{"key":"29_CR21","unstructured":"Statistics, L.B., Breiman, L.: Random forests. In: Machine Learning, pp. 5\u201332 (2001)"},{"issue":"1","key":"29_CR22","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1177\/0272989X0102100106","volume":"21","author":"EW Steyerberg","year":"2001","unstructured":"Steyerberg, E.W., Eijkemans, M., Harrell, F.E., Habbema, J.: Prognostic modeling with logistic regression analysis: in search of a sensible strategy in small data sets. Med. Decis. Making 21(1), 45\u201356 (2001)","journal-title":"Med. Decis. Making"},{"key":"29_CR23","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.procs.2020.02.248","volume":"168","author":"A Subasi","year":"2020","unstructured":"Subasi, A., Kadasa, B., Kremic, E.: Classification of the cardiotocogram data for anticipation of fetal risks using bagging ensemble classifier. Procedia Comput. Sci. 168, 34\u201339 (2020)","journal-title":"Procedia Comput. Sci."},{"key":"29_CR24","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1111\/eva.12524","volume":"11","author":"EV Sylvester","year":"2018","unstructured":"Sylvester, E.V., et al.: Applications of random forest feature selection for fine-scale genetic population assignment. Evol. Appl. 11, 153\u2013165 (2018)","journal-title":"Evol. Appl."},{"issue":"12","key":"29_CR25","doi-asserted-by":"publisher","first-page":"1068","DOI":"10.1111\/j.1471-0528.1980.tb04475.x","volume":"87","author":"T Wheeler","year":"2010","unstructured":"Wheeler, T., Gennser, G., Lindvall, R., Murrills, A.J.: Changes in the fetal heart rate associated with fetal breathing and fetal movement. BJOG: Int. J. Obstet. Gynaecol. 87(12), 1068\u20131079 (2010)","journal-title":"BJOG: Int. J. Obstet. Gynaecol."},{"key":"29_CR26","doi-asserted-by":"publisher","first-page":"754","DOI":"10.4028\/www.scientific.net\/AMM.26-28.754","volume":"26\u201328","author":"FQ Zhao","year":"2010","unstructured":"Zhao, F.Q., Zou, J.H., Yang, Y.H.: A hybrid approach based on artificial neural network (ANN) and differential evolution (DE) for job-shop scheduling problem. Appl. Mech. Mater. 26\u201328, 754\u2013757 (2010)","journal-title":"Appl. Mech. Mater."},{"issue":"1","key":"29_CR27","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.ymssp.2013.12.013","volume":"46","author":"Q Zhou","year":"2014","unstructured":"Zhou, Q., Hao, Z., Zhou, Q., Fan, Y., Luo, L.: Structure damage detection based on random forest recursive feature elimination. Mech. Syst. Sig. Process. 46(1), 82\u201390 (2014)","journal-title":"Mech. Syst. Sig. Process."},{"issue":"458","key":"29_CR28","first-page":"15","volume":"458","author":"J Zou","year":"2009","unstructured":"Zou, J., Han, Y., So, S.S.: Overview of artificial neural networks. Meth. Mol. Biol. 458(458), 15 (2009)","journal-title":"Meth. Mol. Biol."}],"container-title":["Lecture Notes in Computer Science","Advances in Swarm Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-78811-7_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T23:25:05Z","timestamp":1625613905000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-78811-7_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030788100","9783030788117"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-78811-7_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Swarm Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Qingdao","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"swarm2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.iasei.org\/icsi2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"177","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":"104","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":"59% - 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":"2,5","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":"4-5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}