{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T03:22:37Z","timestamp":1743132157409,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031477140"},{"type":"electronic","value":"9783031477157"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-47715-7_38","type":"book-chapter","created":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T20:02:44Z","timestamp":1706558564000},"page":"555-570","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Explainable Dynamic Ensemble Framework for\u00a0Classification Based on\u00a0the\u00a0Late Fusion of\u00a0Heterogeneous Multimodal Data"],"prefix":"10.1007","author":[{"given":"Firuz","family":"Juraev","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaker","family":"El-Sappagh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tamer","family":"Abuhmed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,30]]},"reference":[{"key":"38_CR1","unstructured":"Bonaccorso, G.: Machine Learning Algorithms. Packt Publishing Ltd (2017)"},{"key":"38_CR2","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/BF00058655","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L.: Bagging predictors. Mach. Learn. 24, 123\u2013140 (1996)","journal-title":"Mach. Learn."},{"key":"38_CR3","doi-asserted-by":"crossref","unstructured":"Cao, Y., Geddes, T.A., Yang, J.Y.H., Yang, P.: Ensemble deep learning in bioinformatics. Nat. Mach. Intell. 2(9), 500\u2013508 (2020)","DOI":"10.1038\/s42256-020-0217-y"},{"key":"38_CR4","doi-asserted-by":"crossref","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: synthetic minority over-sampling technique: smote. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","DOI":"10.1613\/jair.953"},{"key":"38_CR5","unstructured":"Cruz, R.M.O., Hafemann, L.G., Sabourin, R., Cavalcanti., G.D.C.: Deslib: a dynamic ensemble selection library in python. J. Mach. Learn. Res. 21(1), 283\u2013287 (2020)"},{"key":"38_CR6","doi-asserted-by":"crossref","unstructured":"Cruz, R.M.O., Sabourin, R., Cavalcanti, G.D.C.: Recent advances and perspectives: dynamic classifier selection. Inf. Fusion 41, 195\u2013216 (2018)","DOI":"10.1016\/j.inffus.2017.09.010"},{"key":"38_CR7","doi-asserted-by":"crossref","unstructured":"El-Rashidy, N., Abuhmed, T., Alarabi, L., El-Bakry, H.M., Abdelrazek, S., Ali, F., El-Sappagh, S.: Sepsis prediction in intensive care unit based on genetic feature optimization and stacked deep ensemble learning. Neural Computing and Applications, pp. 1\u201330 (2022)","DOI":"10.1007\/s00521-021-06631-1"},{"key":"38_CR8","doi-asserted-by":"crossref","unstructured":"El-Rashidy, N., El-Sappagh, S., Abuhmed, T., Abdelrazek, S., El-Bakry, H.M.: An improved patient-specific stacking ensemble model: intensive care unit mortality prediction. IEEE Access 8, 133541\u2013133564 (2020)","DOI":"10.1109\/ACCESS.2020.3010556"},{"key":"38_CR9","doi-asserted-by":"crossref","unstructured":"El-Sappagh, S., Abuhmed, T., Riazul Islam, S.M., Kwak, K.S.: Multimodal multitask deep learning model for alzheimer\u2019s disease progression detection based on time series data. Neurocomputing, 412, 197\u2013215 (2020)","DOI":"10.1016\/j.neucom.2020.05.087"},{"key":"38_CR10","doi-asserted-by":"crossref","unstructured":"El-Sappagh, S., Ali, F., Abuhmed, F., Singh, J., Alonso, J.M.: Automatic detection of alzheimer\u2019s disease progression: an efficient information fusion approach with heterogeneous ensemble classifiers. Neurocomputing 512, 203\u2013224 (2022)","DOI":"10.1016\/j.neucom.2022.09.009"},{"issue":"1","key":"38_CR11","first-page":"3133","volume":"15","author":"M Fern\u00e1ndez-Delgado","year":"2014","unstructured":"Fern\u00e1ndez-Delgado, M., Cernadas, E., Barro, S., Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res. 15(1), 3133\u20133181 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"38_CR12","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"38_CR13","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, 389\u2013422 (2002)","journal-title":"Mach. Learn."},{"key":"38_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2022.104216","volume":"135","author":"F Juraev","year":"2022","unstructured":"Juraev, F., El-Sappagh, S., Abdukhamidov, E., Ali, F., Abuhmed, T.: Multilayer dynamic ensemble model for intensive care unit mortality prediction of neonate patients. J. Biomed. Inf. 135, 104216 (2022)","journal-title":"J. Biomed. Inf."},{"key":"38_CR15","doi-asserted-by":"crossref","unstructured":"Ko, A.H.R., Sabourin, R., Britto\u00a0Jr, A.S.: From dynamic classifier selection to dynamic ensemble selection. Pattern Recognit. 41(5), 1718\u20131731 (2008)","DOI":"10.1016\/j.patcog.2007.10.015"},{"key":"38_CR16","unstructured":"Mahesh, B.: Machine learning algorithms-a review. Int. J. Sci. Res. (IJSR).[Internet] 9, 381\u2013386 (2020)"},{"key":"38_CR17","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"38_CR18","doi-asserted-by":"crossref","unstructured":"Raffa, J.D., Johnson, A.E.W., O\u2019Brien, Z., Pollard, T.J., Mark, R.G., Celi, L.A., Pilcher, D., Badawi, O.: The global open source severity of illness score (gossis). Crit. Care Med. 50(7), 1040\u20131050 (2022)","DOI":"10.1097\/CCM.0000000000005518"},{"key":"38_CR19","doi-asserted-by":"crossref","unstructured":"Raschka, S.: Mlxtend: providing machine learning and data science utilities and extensions to python\u2019s scientific computing stack. J. Open Source Softw. 3(24) (2018)","DOI":"10.21105\/joss.00638"},{"issue":"1","key":"38_CR20","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.inffus.2004.04.008","volume":"6","author":"D Ruta","year":"2005","unstructured":"Ruta, D., Gabrys, B.: Classifier selection for majority voting. Inf. Fusion 6(1), 63\u201381 (2005)","journal-title":"Inf. Fusion"},{"key":"38_CR21","doi-asserted-by":"crossref","unstructured":"Sabourin, M., Mitiche, A., Thomas, D., Nagy, G.: Classifier combination for hand-printed digit recognition. In: Proceedings of 2nd international conference on document analysis and recognition (ICDAR\u201993), pp. 163\u2013166. IEEE (1993)","DOI":"10.1109\/ICDAR.1993.395758"},{"issue":"4","key":"38_CR22","volume":"8","author":"O Sagi","year":"2018","unstructured":"Sagi, O., Rokach, L.: Ensemble learning: a survey. Wiley Interdiscip. Rev.: Data Min. Knowl. Disc. 8(4), e1249 (2018)","journal-title":"Wiley Interdiscip. Rev.: Data Min. Knowl. Disc."},{"key":"38_CR23","unstructured":"Sakkis, G., Androutsopoulos, I., Paliouras, G., Karkaletsis, V., Spyropoulos, C.D., Stamatopoulos, P.: Stacking classifiers for anti-spam filtering of e-mail (2001). cs\/ arXiv:0106040"},{"key":"38_CR24","doi-asserted-by":"crossref","unstructured":"Sammut, C., Webb, G.I. (eds.) Holdout Evaluation, pp. 506\u2013507. Springer, US, Boston, MA (2010)","DOI":"10.1007\/978-0-387-30164-8_369"},{"key":"38_CR25","unstructured":"Schapire, R.E.: A brief introduction to boosting. In: Ijcai, vol.\u00a099, pp. 1401\u20131406. Citeseer (1999)"},{"key":"38_CR26","doi-asserted-by":"crossref","unstructured":"Stahlschmidt, S.R., Ulfenborg, B., Synnergren, J.: Multimodal deep learning for biomedical data fusion: a review. Briefings Bioinform. 23(2), bbab569 (2022)","DOI":"10.1093\/bib\/bbab569"},{"key":"38_CR27","doi-asserted-by":"crossref","unstructured":"Tan, W., Tiwari, P., Pandey, H.M., Moreira, C., Jaiswal, A.M.: Multimodal medical image fusion algorithm in the era of big data. Neural Computing and Applications, pp. 1\u201321 (2020)","DOI":"10.1007\/s00521-020-05173-2"},{"key":"38_CR28","doi-asserted-by":"crossref","unstructured":"Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., Altman, R.B.: Missing value estimation methods for DNA microarrays. Bioinformatics 17(6), 520\u2013525 (2001)","DOI":"10.1093\/bioinformatics\/17.6.520"}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Systems and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-47715-7_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,9]],"date-time":"2024-11-09T13:22:46Z","timestamp":1731158566000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47715-7_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031477140","9783031477157"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47715-7_38","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"30 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IntelliSys","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Intelligent Systems Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Amsterdam","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"intellisys12023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}