{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T08:58:10Z","timestamp":1743152290795,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031284717"},{"type":"electronic","value":"9783031284724"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-28472-4_19","type":"book-chapter","created":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T14:03:08Z","timestamp":1679061788000},"page":"304-319","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Adaptive and Dynamic Heterogeneous Ensemble Model for Credit Scoring"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0356-5245","authenticated-orcid":false,"given":"Tinofirei","family":"Museba","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,18]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.mar.2015.10.002","volume":"32","author":"A Caldarelli","year":"2016","unstructured":"Caldarelli, A., Fiondella, C., Maffei, M., Zagaria, C.: Managing risk in credit cooperative banks: lessons from a case study. Manage. Account. Res. 32, 1\u201315 (2016)","journal-title":"Manage. Account. Res."},{"key":"19_CR2","doi-asserted-by":"crossref","unstructured":"Frame, W.S., Srinivasan, A., Woosley, L.: The effect of credit scoring on small business lending. J. Money Credit Banking 813\u2013825 (2001)","DOI":"10.2307\/2673896"},{"key":"19_CR3","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1016\/j.ejor.2006.09.100","volume":"183","author":"JN Crook","year":"2007","unstructured":"Crook, J.N., Edelman, D.B., Thomas, L.C.: Recent developments in consumer credit risk assessment. Eur. J. Oper. Res. 183, 1447\u20131465 (2007)","journal-title":"Eur. J. Oper. Res."},{"issue":"1","key":"19_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10994-013-5425-9","volume":"95","author":"C Rudin","year":"2014","unstructured":"Rudin, C., Wagstaff, K.L.: Machine learning for science and society. Mach. Learn. 95(1), 1\u20139 (2014)","journal-title":"Mach. Learn."},{"issue":"1","key":"19_CR5","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.eswa.2010.06.048","volume":"38","author":"G Wang","year":"2011","unstructured":"Wang, G., Hao, J., Ma, J., Jiang, H.: A comparative assessment of ensemble learning for credit scoring. Expert syst. Appl. 38(1), 223\u2013230 (2011)","journal-title":"Expert syst. Appl."},{"key":"19_CR6","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.inffus.2011.12.001","volume":"16","author":"CF Tsai","year":"2014","unstructured":"Tsai, C.F.: Combining cluster analysis with cluster ensembles to predict financial distress. Inf. Fusion 16, 46\u201358 (2014)","journal-title":"Inf. Fusion"},{"key":"19_CR7","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.asoc.2016.02.022","volume":"43","author":"H Xiao","year":"2016","unstructured":"Xiao, H., Xiao, Z., Wang, Y.: Ensemble classification based on supervised clustering for credit scoring. Appl. Soft Comput. 43, 73\u201386 (2016)","journal-title":"Appl. Soft Comput."},{"key":"19_CR8","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.eswa.2018.01.012","volume":"98","author":"H He","year":"2018","unstructured":"He, H., Zhang, W., Zhang, S.: A novel ensemble method for credit scoring: adaption of different imbalance ratios. Expert Syst. Appl. 98, 105\u2013117 (2018)","journal-title":"Expert Syst. Appl."},{"key":"19_CR9","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.eswa.2022.116913","volume":"198","author":"J Yao","year":"2022","unstructured":"Yao, J., Zhongyi Wang, L., Wang, M.L., Jiang, H., Chen, Y.: A novel hybrid ensemble credit scoring model with stacking based noise detection and weight assignment. Expert Syst. Appl. 198, 15 (2022)","journal-title":"Expert Syst. Appl."},{"key":"19_CR10","doi-asserted-by":"publisher","first-page":"107687","DOI":"10.1016\/j.asoc.2021.107687","volume":"111","author":"I Singh","year":"2021","unstructured":"Singh, I., Kumar, N., Srinivasa, K.G., Maini, S., Ahuja, U., Jain, S.: A multi-level classification and modified PSO clustering based ensemble approach for credit scoring. Appl. Soft Comput. 111, 107687 (2021)","journal-title":"Appl. Soft Comput."},{"key":"19_CR11","doi-asserted-by":"publisher","first-page":"106462","DOI":"10.1016\/j.knosys.2020.106462","volume":"208","author":"WH Hou","year":"2020","unstructured":"Hou, W.H., Wang, X.K., Zhang, H.Y., Wang, J.Q., Li, L.: A novel dynamic ensemble selection classifier for an imbalanced dataset: an application for credit risk assessment. Knowl. Based Syst. 208, 106462 (2020)","journal-title":"Knowl. Based Syst."},{"key":"19_CR12","doi-asserted-by":"publisher","first-page":"101130","DOI":"10.1016\/j.aei.2020.101130","volume":"45","author":"J Nalic","year":"2020","unstructured":"Nalic, J., Martinovia, G., Zagar, D.: New hybrid data mining model for credit scoring based on feature selection algorithm and ensemble classifiers. Adv. Eng. Inform. 45, 101130 (2020)","journal-title":"Adv. Eng. Inform."},{"key":"19_CR13","doi-asserted-by":"publisher","first-page":"113615","DOI":"10.1016\/j.eswa.2020.113615","volume":"159","author":"Y Xia","year":"2020","unstructured":"Xia, Y., Zhao, J., He, L., Li, Y., Niu, M.: A novel tree-based dynamic heterogeneous ensemble method for credit scoring. Expert Syst. Appl. 159, 113615 (2020)","journal-title":"Expert Syst. Appl."},{"key":"19_CR14","doi-asserted-by":"publisher","first-page":"113899","DOI":"10.1016\/j.eswa.2020.113899","volume":"162","author":"JP Barddal","year":"2020","unstructured":"Barddal, J.P., Loezer, L., Enembreck, F., Lanzuolo, R.: Lessons learned from data stream classification applied to credit scoring. Expert Syst. Appl. 162, 113899 (2020)","journal-title":"Expert Syst. Appl."},{"key":"19_CR15","first-page":"15","volume":"189","author":"X Jin Xiao","year":"2020","unstructured":"Jin Xiao, X., Zhong, Z.Y., Xie, L., Xin, G., Liu, D.: Cost-Sensitive semi-supervised selective ensemble model for customer credit scoring. Knowl. Based Syst. 189, 15 (2020)","journal-title":"Knowl. Based Syst."},{"key":"19_CR16","doi-asserted-by":"publisher","first-page":"222449","DOI":"10.1109\/ACCESS.2020.3043937","volume":"8","author":"X Chen","year":"2020","unstructured":"Chen, X., Li, S., Xu, X., Meng, F., Cao, W.: A novel GSCI-based ensemble approach for credit scoring. IEEE Access 8, 222449\u2013222465 (2020)","journal-title":"IEEE Access"},{"key":"19_CR17","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"},{"issue":"12","key":"19_CR18","doi-asserted-by":"publisher","first-page":"2067","DOI":"10.3390\/en10122067","volume":"10","author":"SX Wang","year":"2017","unstructured":"Wang, S.X., Dong, P.F., Tian, Y.J.: A novel method of statistical line loss estimation for distribution feeders based on feeder cluster and modified XGBoost. Energies 10(12), 2067 (2017)","journal-title":"Energies"},{"key":"19_CR19","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":"19_CR20","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.eswa.2017.02.017","volume":"78","author":"Y Xia","year":"2017","unstructured":"Xia, Y., Li, C., Liu, N.: A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Syst. Appl. 78, 225\u2013241 (2017)","journal-title":"Expert Syst. Appl."},{"issue":"23","key":"19_CR21","doi-asserted-by":"publisher","first-page":"6803","DOI":"10.1080\/03610926.2014.968730","volume":"45","author":"KH Chang","year":"2016","unstructured":"Chang, K.H., Chu, H.H., Tong, L.I.: Establish decision tree-based short term default credit risk assessment models. Commun. Stat. Theory Methods 45(23), 6803\u20136815 (2016)","journal-title":"Commun. Stat. Theory Methods"},{"key":"19_CR22","volume-title":"Computational Intelligence: An introduction","author":"AP Engelbrecht","year":"2002","unstructured":"Engelbrecht, A.P.: Computational Intelligence: An introduction. Wiley, Chichester (2002)"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, 27 November 27\u20131 December 1995, vol. 4 pp. 1942\u20131948 (1995)","DOI":"10.1109\/ICNN.1995.488968"},{"key":"19_CR24","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.inffus.2017.02.010","volume":"38","author":"RM Cruz","year":"2017","unstructured":"Cruz, R.M., Sabourin, R., Cavalcanti, G.D.: META-DES oracle: meta-learning and feature selection for dynamic ensemble selection. Inf. Fusion 38, 84\u2013103 (2017)","journal-title":"Inf. Fusion"},{"issue":"4","key":"19_CR25","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1109\/TKDE.2011.58","volume":"24","author":"LL Minku","year":"2012","unstructured":"Minku, L.L., Yao, X.: DDD: a new ensemble approach for dealing with concept drift. IEEE Trans. Knowl. Data Eng. 24(4), 619\u2013633 (2012)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"19_CR26","doi-asserted-by":"publisher","first-page":"4266","DOI":"10.1016\/j.proeng.2011.08.800","volume":"15","author":"L Yang","year":"2011","unstructured":"Yang, L.: Classifier selection for ensemble learning based on accuracy and diversity. Proc. Eng. 15, 4266\u20134270 (2011)","journal-title":"Proc. Eng."},{"key":"19_CR27","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1098\/rsta.1900.0019","volume":"194","author":"G Yule","year":"1900","unstructured":"Yule, G.: On the association of attributes in statistics. Philos. Trans. Roy. Soc. London Ser. A 194, 257\u2013319 (1900)","journal-title":"Philos. Trans. Roy. Soc. London Ser. A"},{"key":"19_CR28","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1016\/S0378-4266(03)00202-4","volume":"28","author":"RB Avery","year":"2004","unstructured":"Avery, R.B., Calem, P.S., Canner, G.B.: Consumer credit scoring: do situational circumstances matter? J. Bank. Finance 28, 835\u2013856 (2004)","journal-title":"J. Bank. Finance"},{"key":"19_CR29","unstructured":"Asuncion, A., Newman, D.: UCI Machine Learning Repository. Publishing (2007)"},{"key":"19_CR30","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.eswa.2018.12.020","volume":"121","author":"W Zhang","year":"2019","unstructured":"Zhang, W., He, H., Zhang, S.: A novel multi-stage hybrid model with enhanced multi-population niche genetic algorithm: an application in credit scoring. Expert Syst. Appl. 121, 221\u2013232 (2019)","journal-title":"Expert Syst. Appl."},{"key":"19_CR31","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.ejor.2015.05.030","volume":"247","author":"S Lessmann","year":"2015","unstructured":"Lessmann, S., Baesens, B., Seow, H.V., Thomas, L.C.: Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research. Eur. J. Oper. Res. 247, 124\u2013136 (2015)","journal-title":"Eur. J. Oper. Res."},{"key":"19_CR32","first-page":"1","volume":"7","author":"J Demsar","year":"2006","unstructured":"Demsar, J.: Statistical comparison of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1\u201330 (2006)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Communications in Computer and Information Science","Digital-for-Development: Enabling Transformation, Inclusion and Sustainability Through ICTs"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-28472-4_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T16:07:27Z","timestamp":1729094847000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-28472-4_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031284717","9783031284724"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-28472-4_19","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"18 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The author declares that he has no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration of Competing Interests"}},{"value":"IDIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Development Informatics Association Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mbombela","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"South Africa","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"idia2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/idia2022.com\/","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":"61","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":"20","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":"33% - 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":"2","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)"}}]}}