{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T11:59:10Z","timestamp":1769255950381,"version":"3.49.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s00521-024-10716-y","type":"journal-article","created":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T12:44:32Z","timestamp":1733834672000},"page":"2925-2943","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Hierarchical cluster-based IELM for financial distress prediction with imbalanced data"],"prefix":"10.1007","volume":"37","author":[{"given":"Amal Ibrahim Al","family":"Ali","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Sheeja Rani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P. V.","family":"Pravija Raj","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7957-7862","authenticated-orcid":false,"given":"Ahmed M.","family":"Khedr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,10]]},"reference":[{"key":"10716_CR1","doi-asserted-by":"publisher","first-page":"2089","DOI":"10.1007\/s13042-022-01566-y","volume":"13","author":"Ying Chen","year":"2022","unstructured":"Chen Ying, Guo Jifeng, Huang Junqin, Lin Bin (2022) A novel method for financial distress prediction based on sparse neural networks with L1\/2 regularization. Int J Mach Learn Cybern, Springer 13:2089\u20132103. https:\/\/doi.org\/10.1007\/s13042-022-01566-y","journal-title":"Int J Mach Learn Cybern, Springer"},{"key":"10716_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dss.2022.113814","volume":"159","author":"Wu Desheng","year":"2022","unstructured":"Desheng Wu, Ma Xiyuan, Olson David L (2022) Financial distress prediction using integrated Z-score and multilayer perceptron neural networks. Decis Support Syst 159:1\u20138. https:\/\/doi.org\/10.1016\/j.dss.2022.113814","journal-title":"Decis Support Syst"},{"issue":"1","key":"10716_CR3","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1109\/TBDATA.2019.2907624","volume":"7","author":"Joelson Antonio","year":"2021","unstructured":"Antonio Joelson, dos Santos Talat, Syed Iqbal, Naldi Murilo C, Campello Ricardo J. G. B, Sander Joerg (2021) Hierarchical density-based clustering using mapReduce. IEEE Trans Big Data 7(1):102\u2013114. https:\/\/doi.org\/10.1109\/TBDATA.2019.2907624","journal-title":"IEEE Trans Big Data"},{"key":"10716_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/s10690-022-09387-3","author":"Taicir Mezghani","year":"2022","unstructured":"Mezghani Taicir, Abbes Mouna Boujelb\u00e9ne (2022) Forecast the role of GCC financial stress on oil market and GCC financial markets using convolutional neural networks. Asia-Pacific Finan Markets. https:\/\/doi.org\/10.1007\/s10690-022-09387-3","journal-title":"Asia-Pacific Finan Markets"},{"key":"10716_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.eswa.2022.117271","volume":"202","author":"C Andreas","year":"2022","unstructured":"Andreas C, Bueff Mateusz Cytry\u0144ski, Calabrese Raffaella, Jones Matthew, Roberts John, Moore Jonathon, Brown Iain (2022) Machine learning interpretability for a stress scenario generation in credit scoring based on counterfactuals. Expert Syst Appl 202:1\u201314. https:\/\/doi.org\/10.1016\/j.eswa.2022.117271","journal-title":"Expert Syst Appl"},{"key":"10716_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-023-08470-8","author":"Petr Hajek","year":"2023","unstructured":"Hajek Petr, Munk Michal (2023) Speech emotion recognition and text sentiment analysis for financial distress prediction. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-023-08470-8","journal-title":"Neural Comput Appl"},{"key":"10716_CR7","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.ins.2021.01.059","volume":"559","author":"Jie Sun","year":"2021","unstructured":"Sun Jie, Fujita Hamido, Zheng Yujiao, Ai Wenguo (2021) Multi-class financial distress prediction based on support vector machines integrated with the decomposition and fusion methods. Inf Sci 559:153\u2013170. https:\/\/doi.org\/10.1016\/j.ins.2021.01.059","journal-title":"Inf Sci"},{"key":"10716_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-022-04766-5","author":"Mohamed Elhoseny","year":"2022","unstructured":"Elhoseny Mohamed, Metawa Noura, Sztano Gabor, El-hasnony Ibrahim M (2022) Deep learning-based model for financial distress prediction. Annal Oper Res. https:\/\/doi.org\/10.1007\/s10479-022-04766-5","journal-title":"Annal Oper Res"},{"issue":"1","key":"10716_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/ijfs11010038","volume":"11","author":"Amal Al Ali","year":"2023","unstructured":"Ali Amal Al, Khedr Ahmed M, El Bannany Magdi, Kanakkayil Sakeena (2023) GALSTM-FDP: a time-series modeling approach using hybrid GA and LSTM for financial distress prediction. Int J Finan Stud 11(1):1\u201315. https:\/\/doi.org\/10.3390\/ijfs11010038","journal-title":"Int J Finan Stud"},{"issue":"3","key":"10716_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/sym13030443","volume":"13","author":"Chyan-long Jan","year":"2021","unstructured":"Jan Chyan-long (2021) Financial information asymmetry: using deep learning algorithms to predict financial distress. Symmetry 13(3):1\u201322. https:\/\/doi.org\/10.3390\/sym13030443","journal-title":"Symmetry"},{"key":"10716_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2023\/6259689","volume":"2023","author":"Jie Zhu","year":"2023","unstructured":"Zhu Jie, Zhu Hong, Lin Nan (2023) A dynamic prediction model of financial distress in the financial sharing environment. Discrete Dyn Nat Soc 2023:1\u201311. https:\/\/doi.org\/10.1155\/2023\/6259689","journal-title":"Discrete Dyn Nat Soc"},{"key":"10716_CR12","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1016\/j.procs.2020.03.054","volume":"170","author":"Said Marso","year":"2020","unstructured":"Marso Said, El Merouani Mohamed (2020) Predicting financial distress using hybrid feedforward neural network with cuckoo search algorithm. Procedia Comput Sci 170:1134\u20131140. https:\/\/doi.org\/10.1016\/j.procs.2020.03.054","journal-title":"Procedia Comput Sci"},{"key":"10716_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/5625271","volume":"2020","author":"Sen Zeng","year":"2020","unstructured":"Zeng Sen, Li Yaqin, Yang Wanjun, Li Yanru (2020) A financial distress prediction model based on sparse algorithm and support vector machine. Math Probl Eng 2020:1\u201311. https:\/\/doi.org\/10.1155\/2020\/5625271","journal-title":"Math Probl Eng"},{"key":"10716_CR14","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.jbusres.2021.03.018","volume":"130","author":"Chih-Fong Tsai","year":"2021","unstructured":"Tsai Chih-Fong, Sue Kuen-Liang, Ya-Han Hu, Chiu Andy (2021) Combining feature selection, instance selection, and ensemble classification techniques for improved financial distress prediction. J Bus Res 130:200\u2013209. https:\/\/doi.org\/10.1016\/j.jbusres.2021.03.018","journal-title":"J Bus Res"},{"key":"10716_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.mlwa.2022.100432","volume":"10","author":"Nezir Aydin","year":"2022","unstructured":"Aydin Nezir, Sahin Nida, Deveci Muhammet, Pamucar Dragan (2022) Prediction of financial distress of companies with artificial neural networks and decision trees models. Mach Learn Appl 10:1\u201313. https:\/\/doi.org\/10.1016\/j.mlwa.2022.100432","journal-title":"Mach Learn Appl"},{"issue":"14","key":"10716_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/app12146918","volume":"12","author":"Salah Al-Deen Safi","year":"2022","unstructured":"Safi Salah Al-Deen, Castillo Pedro A, Faris Hossam (2022) Cost-sensitive metaheuristic optimization-based neural network with ensemble learning for financial distress prediction. Appl Sci 12(14):1\u201332. https:\/\/doi.org\/10.3390\/app12146918","journal-title":"Appl Sci"},{"issue":"1","key":"10716_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/20430795.2021.2017257","volume":"13","author":"Fikile Dubea","year":"2023","unstructured":"Dubea Fikile, Nzimandeb Ntokozo, Muzindutsi Paul-Francois (2023) Application of artificial neural networks in predicting financial distress in the JSE financial services and manufacturing companies. J Sustain Fin Invest 13(1):1\u201322. https:\/\/doi.org\/10.1080\/20430795.2021.2017257","journal-title":"J Sustain Fin Invest"},{"issue":"11","key":"10716_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/data7110160","volume":"7","author":"Kim Long Tran","year":"2022","unstructured":"Tran Kim Long, Le Hoang Anh, Nguyen Thanh Hien, Nguyen Duc Trung (2022) Explainable machine learning for financial distress prediction: evidence from Vietnam. Data 7(11):1\u201312. https:\/\/doi.org\/10.3390\/data7110160","journal-title":"Data"},{"key":"10716_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.econmod.2021.105709","volume":"106","author":"Shuping Zhao","year":"2022","unstructured":"Zhao Shuping, Kai Xu, Wang Zhao, Liang Changyong, Wenxing Lu, Chen Bo (2022) Financial distress prediction by combining sentiment tone features. Econ Model 106:1\u201310. https:\/\/doi.org\/10.1016\/j.econmod.2021.105709","journal-title":"Econ Model"},{"key":"10716_CR20","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/9038992","author":"Lin Zhu","year":"2022","unstructured":"Zhu Lin, Yan Dawen, Zhang Zhihua, Chi Guotai (2022) Financial distress prediction of Chinese listed companies using the combination of optimization model and convolutional neural network. Math Probl Eng. https:\/\/doi.org\/10.1155\/2022\/9038992","journal-title":"Math Probl Eng"},{"issue":"1","key":"10716_CR21","doi-asserted-by":"publisher","first-page":"4","DOI":"10.5755\/j01.ee.32.1.27382","volume":"32","author":"Aidas Malakauskas","year":"2021","unstructured":"Malakauskas Aidas, Lakstutiene Ausrine (2021) Financial distress prediction for small and medium enterprises using machine learning techniques. Inzinerine Ekonomika-Engineering Economics 32(1):4\u201314. https:\/\/doi.org\/10.5755\/j01.ee.32.1.27382","journal-title":"Inzinerine Ekonomika-Engineering Economics"},{"key":"10716_CR22","doi-asserted-by":"crossref","unstructured":"Nazari Z, Kang D, Asharif MR, Sung Y, Ogawa S (2015) A new hierarchical clustering algorithm. In: International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","DOI":"10.1109\/ICIIBMS.2015.7439517"},{"issue":"2","key":"10716_CR23","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.13485","volume":"41","author":"El Madou","year":"2024","unstructured":"Madou El, Kaoutar Said Marso, El Kharrim Moad, El Merouani Mohamed (2024) Evolutions in machine learning technology for financial distress prediction: a comprehensive review and comparative analysis. Expert Syst 41(2):e13485","journal-title":"Expert Syst"},{"key":"10716_CR24","doi-asserted-by":"crossref","unstructured":"Strelcenia E, Prakoonwit S (2023) A New GAN-based data augmentation method for Handling Class Imbalance in Credit Card Fraud detection. In: 2023 10th International Conference on Signal Processing and Integrated Networks (SPIN), IEEE, pp 627-634","DOI":"10.1109\/SPIN57001.2023.10116543"},{"issue":"12","key":"10716_CR25","doi-asserted-by":"publisher","first-page":"5809","DOI":"10.1109\/TKDE.2021.3061428","volume":"34","author":"Kaixiang Yang","year":"2021","unstructured":"Yang Kaixiang, Zhiwen Yu CL, Chen Philip, Cao Wenming, You Jane, Wong Hau-San (2021) Incremental weighted ensemble broad learning system for imbalanced data. IEEE Trans Knowl Data Eng 34(12):5809\u20135824","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10716_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cose.2021.102289","volume":"106","author":"Jingmei Liu","year":"2021","unstructured":"Liu Jingmei, Gao Yuanbo, Fengjie Hu (2021) A fast network intrusion detection system using adaptive synthetic oversampling and LightGBM. Comput Secur 106:1\u201315","journal-title":"Comput Secur"},{"key":"10716_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.asoc.2022.108618","volume":"120","author":"Dongxia Meng","year":"2022","unstructured":"Meng Dongxia, Li Yujian (2022) An imbalanced learning method by combining SMOTE with center offset factor. Appl Soft Comput 120:1\u201315","journal-title":"Appl Soft Comput"},{"issue":"25","key":"10716_CR28","first-page":"1","volume":"223","author":"Rahul Mitra","year":"2023","unstructured":"Mitra Rahul, Bajpai Anurag, Biswas Krishanu (2023) ADASYN-assisted machine learning for phase prediction of high entropy carbides. Comput Mater Sci 223(25):1\u201315","journal-title":"Comput Mater Sci"},{"key":"10716_CR29","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1016\/j.procs.2024.04.095","volume":"235","author":"TA Admassu","year":"2024","unstructured":"Admassu TA, Salau AO, Sampath K, Govindarajan R, Murugan S, Lakshmi B (2024) Evaluation of adaptive synthetic resampling technique for imbalanced breast cancer identification. Procedia Comput Sci 235:1000\u20131007","journal-title":"Procedia Comput Sci"},{"key":"10716_CR30","first-page":"1","volume":"115","author":"Islam Ashhadul","year":"2022","unstructured":"Ashhadul Islam, Belhaouari Samir Brahim, Rehman Atiq Ur, Bensmail Halima (2022) KNNOR: an oversampling technique for imbalanced datasets. Appl Soft Comput 115:1\u201315","journal-title":"Appl Soft Comput"},{"key":"10716_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107378","volume":"107","author":"Du Hongle","year":"2021","unstructured":"Hongle Du, Zhang Yan, Gang Ke, Zhang Lin, Chen Yeh-Cheng (2021) Online ensemble learning algorithm for imbalanced data stream. Appl Soft Comput 107:107378","journal-title":"Appl Soft Comput"},{"key":"10716_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107884","volume":"113","author":"Zhe Wang","year":"2021","unstructured":"Wang Zhe, Jia Peng, Xinlei Xu, Wang Bolu, Zhu Yujin, Li Dongdong (2021) Sample and feature selecting based ensemble learning for imbalanced problems. Appl Soft Comput 113:107884","journal-title":"Appl Soft Comput"},{"key":"10716_CR33","first-page":"5","volume":"203","author":"Ju Jiakun Zhao","year":"2020","unstructured":"Jiakun Zhao Ju, Jin Si Chen, Zhang Ruifeng, Bilin Yu, Liu Qingfang (2020) A weighted hybrid ensemble method for classifying imbalanced data. Knowl-Based Syst 203:5","journal-title":"Knowl-Based Syst"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10716-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10716-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10716-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T08:58:21Z","timestamp":1738918701000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10716-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,10]]},"references-count":33,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["10716"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10716-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,10]]},"assertion":[{"value":"25 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}