{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T18:14:25Z","timestamp":1764785665553,"version":"3.44.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T00:00:00Z","timestamp":1753142400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T00:00:00Z","timestamp":1753142400000},"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":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-025-04205-9","type":"journal-article","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T09:27:51Z","timestamp":1753176471000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Optimized Ensemble Learning Framework with Enhanced Feature Selection for Efficient and Accurate Classification"],"prefix":"10.1007","volume":"6","author":[{"given":"M. L.","family":"Asha","sequence":"first","affiliation":[]},{"given":"T.","family":"Johnpeter","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,22]]},"reference":[{"key":"4205_CR1","unstructured":"Statista Research Department, \"Walmart-Statistics & Facts,\" Statista. 2023. [Online]. https:\/\/www.statista.com\/topics\/1451\/walmart\/. Accessed 10 Jan 2025."},{"key":"4205_CR2","unstructured":"NASA, \"NASA Open Data Portal,\" NASA. 2023. [Online]. https:\/\/data.nasa.gov\/. Accessed 10 Jan 2025."},{"key":"4205_CR3","unstructured":"DemandSage, \"Big Data Statistics: 2023 Data Growth & Usage Trends,\" DemandSage. 2023. [Online]. https:\/\/www.demandsage.com\/big-data-statistics\/. Accessed 10 Jan 2025."},{"key":"4205_CR4","unstructured":"Microsoft Research, \"Mining Insights from Search Engine Query Streams,\" Microsoft. 2023. [Online]. https:\/\/www.microsoft.com\/en-us\/research\/publication\/mining-insights-search-engine-query-stream\/. Accessed 10 Jan 2025."},{"key":"4205_CR5","unstructured":"Statista Research Department, \"Number of Tweets Per Day Worldwide,\" Statista. 2023. [Online]. https:\/\/www.statista.com\/statistics\/282087\/number-of-monthly-active-twitter-users\/. Accessed 10 Jan 2025."},{"key":"4205_CR6","doi-asserted-by":"publisher","first-page":"181314","DOI":"10.1109\/ACCESS.2019.2959044","volume":"7","author":"J Read","year":"2019","unstructured":"Read J, Martino L, Luengo D. A survey on multi-label data stream classification. IEEE Access. 2019;7:181314\u201329. https:\/\/doi.org\/10.1109\/ACCESS.2019.2959044.","journal-title":"IEEE Access"},{"key":"4205_CR7","doi-asserted-by":"crossref","unstructured":"Esteban A, Cano A, Zafra A, Ventura S. \"Hoeffding Adaptive Trees for Multi-Label Classification on Data Streams,\" arXiv preprint, Oct. 2024. [Online]. https:\/\/arxiv.org\/abs\/2410.20242.","DOI":"10.1016\/j.knosys.2024.112561"},{"key":"4205_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-024-03091-x","author":"P Sunagar","year":"2024","unstructured":"Sunagar P, et al. Hybrid RNN based text classification model for unstructured data. SN Comput Sci. 2024. https:\/\/doi.org\/10.1007\/s42979-024-03091-x.","journal-title":"SN Comput Sci"},{"issue":"1","key":"4205_CR9","first-page":"123","volume":"19","author":"J Chen","year":"2024","unstructured":"Chen J, Zhang H, Zhou Y. Next-generation big data analytics: state of the art, challenges, and future research topics. IEEE Trans Ind Inf. 2024;19(1):123\u201334.","journal-title":"IEEE Trans Ind Inf"},{"key":"4205_CR10","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/s11276-022-03177-5","volume":"29","author":"H Hassani","year":"2024","unstructured":"Hassani H, Huang X, Silva E. A comprehensive and systematic literature review on the big data management in IoT. Wirel Netw. 2024;29:345\u201367. https:\/\/doi.org\/10.1007\/s11276-022-03177-5.","journal-title":"Wirel Netw"},{"key":"4205_CR11","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.neucom.2015.08.112","volume":"195","author":"Z Deng","year":"2016","unstructured":"Deng Z, Zhu X, Cheng D, Zong M, Zhang S. Efficient kNN classification algorithm for big data. Neurocomputing. 2016;195:143\u20138.","journal-title":"Neurocomputing"},{"key":"4205_CR12","doi-asserted-by":"publisher","unstructured":"P. K. D. Pramanik, S. Pal, M. Mukhopadhyay, and S. P. Singh, \u201cBig Data classification: techniques and tools,\u201d Applications of Big Data\nin Healthcare. Elsevier, pp. 1\u201343, 2021. https:\/\/doi.org\/10.1016\/b978-0-12-820203-6.00002-3","DOI":"10.1016\/b978-0-12-820203-6.00002-3"},{"issue":"9","key":"4205_CR13","doi-asserted-by":"publisher","first-page":"9573","DOI":"10.1109\/TCYB.2021.3061152","volume":"52","author":"X-F Song","year":"2022","unstructured":"Song X-F, Zhang Y, Gong D-W, Gao X-Z. A fast hybrid feature selection based on correlation-guided clustering and particle swarm optimization for high-dimensional data. IEEE Trans Cybern. 2022;52(9):9573\u201386.","journal-title":"IEEE Trans Cybern"},{"key":"4205_CR14","doi-asserted-by":"publisher","unstructured":"Li X, Li  B, Wang Y. \u201cMultiobjective Fuzzy Competitive Swarm Optimization for High-Dimensional Feature Selection,\u201d 2023 5th International Conference on Data-driven Optimization of Complex Systems (DOCS). IEEE, Tianjin, China, pp. 1\u20136, Sept. 22,\n2023. https:\/\/doi.org\/10.1109\/docs60977.2023.10294455.","DOI":"10.1109\/docs60977.2023.10294455"},{"issue":"3","key":"4205_CR15","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1109\/TEVC.2021.3100056","volume":"26","author":"K Chen","year":"2022","unstructured":"Chen K, Xue B, Zhang M, Zhou F. Evolutionary multitasking for feature selection in high-dimensional classification via particle swarm optimization. IEEE Trans Evol Comput. 2022;26(3):446\u201360.","journal-title":"IEEE Trans Evol Comput"},{"issue":"4","key":"4205_CR16","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1109\/TEVC.2023.3254155","volume":"27","author":"L Li","year":"2023","unstructured":"Li L, Xuan M, Lin Q, Jiang M, Ming Z, Tan KC. An evolutionary multitasking algorithm with multiple filtering for high-dimensional feature selection. IEEE Trans Evol Comput. 2023;27(4):802\u201316.","journal-title":"IEEE Trans Evol Comput"},{"key":"4205_CR17","doi-asserted-by":"publisher","first-page":"87918","DOI":"10.1109\/ACCESS.2020.2991800","volume":"8","author":"J Maillo","year":"2020","unstructured":"Maillo J, Triguero I, Herrera F. Redundancy and complexity metrics for big data classification: towards smart data. IEEE Access. 2020;8:87918\u201328. https:\/\/doi.org\/10.1109\/ACCESS.2020.2991800.","journal-title":"IEEE Access"},{"key":"4205_CR18","doi-asserted-by":"publisher","first-page":"190347","DOI":"10.1109\/ACCESS.2024.3510888","volume":"12","author":"W Zhang","year":"2024","unstructured":"Zhang W, et al. Dynamic multi-level competition learning-based dual-task optimization for high-dimensional feature selection. IEEE Access. 2024;12:190347\u201361. https:\/\/doi.org\/10.1109\/ACCESS.2024.3510888.","journal-title":"IEEE Access"},{"issue":"4","key":"4205_CR19","doi-asserted-by":"publisher","first-page":"2261","DOI":"10.1109\/TFUZZ.2023.3347793","volume":"32","author":"L Jara","year":"2024","unstructured":"Jara L, Gonz\u00e1lez A, P\u00e9rez R. QChi: a faster classification algorithm based on Wang\u2013Mendel Algorithm. IEEE Trans Fuzzy Syst. 2024;32(4):2261\u201371. https:\/\/doi.org\/10.1109\/TFUZZ.2023.3347793.","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"3","key":"4205_CR20","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1109\/TBDATA.2023.3327220","volume":"10","author":"C Gong","year":"2024","unstructured":"Gong C, Demmel J, You Y. Scalable evidential K-nearest neighbor classification on big data. IEEE Trans Big Data. 2024;10(3):226\u201337. https:\/\/doi.org\/10.1109\/TBDATA.2023.3327220.","journal-title":"IEEE Trans Big Data"},{"issue":"2","key":"4205_CR21","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1109\/TAI.2023.3254519","volume":"5","author":"J Tian","year":"2024","unstructured":"Tian J, et al. Synergetic focal loss for imbalanced classification in federated XGBoost. IEEE Trans Artif Intell. 2024;5(2):647\u201360. https:\/\/doi.org\/10.1109\/TAI.2023.3254519.","journal-title":"IEEE Trans Artif Intell"},{"key":"4205_CR22","doi-asserted-by":"publisher","first-page":"12592","DOI":"10.1109\/ACCESS.2022.3142888","volume":"10","author":"AB Hassanat","year":"2022","unstructured":"Hassanat AB, et al. Magnetic force classifier: a novel method for big data classification. IEEE Access. 2022;10:12592\u2013606. https:\/\/doi.org\/10.1109\/ACCESS.2022.3142888.","journal-title":"IEEE Access"},{"key":"4205_CR23","doi-asserted-by":"publisher","first-page":"4308","DOI":"10.1038\/ncomms5308","volume":"5","author":"P Baldi","year":"2014","unstructured":"Baldi P, Sadowski P, Whiteson D. Searching for exotic particles in high-energy physics with deep learning. Nat Commun. 2014;5:4308.","journal-title":"Nat Commun"},{"issue":"11","key":"4205_CR24","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278\u2013324. https:\/\/doi.org\/10.1109\/5.726791.","journal-title":"Proc IEEE"},{"issue":"5","key":"4205_CR25","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1109\/34.291440","volume":"16","author":"JJ Hull","year":"1994","unstructured":"Hull JJ. A database for handwritten text recognition research. IEEE Trans Pattern Anal Mach Intell. 1994;16(5):550\u20134. https:\/\/doi.org\/10.1109\/34.291440.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4205_CR26","doi-asserted-by":"publisher","unstructured":"Combining multiple representations for pen-based handwritten digit recognition. In Proceedings of the fourth international conference on document analysis and recognition (ICDAR), Ulm, Germany. 1997. p. 637\u201340. https:\/\/doi.org\/10.1109\/ICDAR.1997.620604.","DOI":"10.1109\/ICDAR.1997.620604"},{"issue":"2","key":"4205_CR27","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/BF00153759","volume":"6","author":"PW Frey","year":"1991","unstructured":"Frey PW, Slate DJ. Letter recognition using Holland-style adaptive classifiers. Mach Learn. 1991;6(2):161\u201382. https:\/\/doi.org\/10.1007\/BF00153759.","journal-title":"Mach Learn"},{"key":"4205_CR28","doi-asserted-by":"publisher","unstructured":"Guyon I, Gunn S, Nikravesh M, Zadeh LA (Eds.). \u201cFeature extraction: foundations and applications,\u201d Studies in fuzziness and soft computing, vol. 207. Springer; 2006. https:\/\/doi.org\/10.1007\/978-3-540-35488-8.","DOI":"10.1007\/978-3-540-35488-8"},{"issue":"4","key":"4205_CR29","doi-asserted-by":"publisher","first-page":"54","DOI":"10.3390\/computers7040054","volume":"7","author":"A Hassanat","year":"2018","unstructured":"Hassanat A. Norm-based binary search trees for speeding up KNN big data classification. Computers. 2018;7(4):54.","journal-title":"Computers"},{"key":"4205_CR30","doi-asserted-by":"publisher","unstructured":"Maillo J, Triguero  I, Herrera F. \u201cA Map Reduce-Based k-Nearest Neighbor Approach for Big Data Classification,\u201d 2015 IEEE Trustcom\/BigDataSE\/ISPA. IEEE, Helsinki, Finland, pp. 167\u2013172, Aug. 2015. https:\/\/doi.org\/10.1109\/trustcom.2015.577","DOI":"10.1109\/trustcom.2015.577"},{"key":"4205_CR31","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.knosys.2016.06.012","volume":"117","author":"J Maillo","year":"2017","unstructured":"Maillo J, Ram\u00edrez S, Triguero I, Herrera F. kNN-IS: an iterative Spark-based design of the k-Nearest Neighbors classifier for big data. Knowl Based Syst. 2017;117:3\u201315.","journal-title":"Knowl Based Syst"},{"issue":"3","key":"4205_CR32","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1089\/big.2018.0064","volume":"6","author":"ABA Hassanat","year":"2018","unstructured":"Hassanat ABA. Furthest-pair-based binary search tree for speeding big data classification using K-nearest neighbors. Big Data. 2018;6(3):225\u201335.","journal-title":"Big Data"},{"key":"4205_CR33","doi-asserted-by":"crossref","unstructured":"Maillo J, Triguero I, Herrera F. A MapReduce-based k-nearest neighbor approach for big data classification. In Proceedings of IEEE Trustcom\/BigDataSE\/ISPA, vol. 2. 2015, p. 167\u201372.","DOI":"10.1109\/Trustcom.2015.577"},{"key":"4205_CR34","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.neucom.2017.12.061","volume":"284","author":"F Wang","year":"2018","unstructured":"Wang F, Wang Q, Nie F, Yu W, Wang R. Efficient tree classifiers for large scale datasets. Neurocomputing. 2018;284:70\u20139.","journal-title":"Neurocomputing"},{"key":"4205_CR35","doi-asserted-by":"publisher","first-page":"89791","DOI":"10.1109\/ACCESS.2023","volume":"11","author":"AS Tarawneh","year":"2023","unstructured":"Tarawneh AS, Alamri ES, Al-Saedi NN, Alauthman M, Hassanat AB. CTELC: a constant-time ensemble learning classifier based on KNN for big data. IEEE Access. 2023;11:89791\u2013802. https:\/\/doi.org\/10.1109\/ACCESS.2023.","journal-title":"IEEE Access"},{"key":"4205_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.108006","volume":"113","author":"A Khoder","year":"2021","unstructured":"Khoder A, Dornaika F. Ensemble learning via feature selection and multiple transformed subsets: application to image classification. Appl Soft Comput. 2021;113: 108006.","journal-title":"Appl Soft Comput"},{"issue":"2","key":"4205_CR37","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1007\/s11227-024-06790-7","volume":"81","author":"AA Ewees","year":"2025","unstructured":"Ewees AA, Alshahrani MM, Alharthi AM, et al. Optimizing feature selection and remote sensing classification with an enhanced machine learning method. J Supercomput. 2025;81(2):370.","journal-title":"J Supercomput"},{"key":"4205_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103587","volume":"137","author":"AV Turukmane","year":"2024","unstructured":"Turukmane AV, Devendiran R. M-MultiSVM: an efficient feature selection assisted network intrusion detection system using machine learning. Comput Secur. 2024;137: 103587.","journal-title":"Comput Secur"},{"key":"4205_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2023.103331","volume":"152","author":"M Osman","year":"2024","unstructured":"Osman M, He J, Zhu N, et al. An ensemble learning framework for the detection of RPL attacks in IoT networks based on the genetic feature selection approach. Ad Hoc Netw. 2024;152: 103331.","journal-title":"Ad Hoc Netw"},{"key":"4205_CR40","doi-asserted-by":"publisher","first-page":"89098","DOI":"10.1109\/ACCESS.2024.3418974","volume":"12","author":"G Obaido","year":"2024","unstructured":"Obaido G, Achilonu O, Ogbuokiri B, et al. An improved framework for detecting thyroid disease using filter-based feature selection and stacking ensemble. IEEE Access. 2024;12:89098\u2013112.","journal-title":"IEEE Access"},{"key":"4205_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2023.114126","volume":"178","author":"S Bouazizi","year":"2024","unstructured":"Bouazizi S, Ltifi H. Enhancing accuracy and interpretability in EEG-based medical decision making using an explainable ensemble learning framework application for stroke prediction. Decis Support Syst. 2024;178: 114126.","journal-title":"Decis Support Syst"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04205-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-025-04205-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04205-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T17:15:08Z","timestamp":1757265308000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-025-04205-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,22]]},"references-count":41,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["4205"],"URL":"https:\/\/doi.org\/10.1007\/s42979-025-04205-9","relation":{},"ISSN":["2661-8907"],"issn-type":[{"type":"electronic","value":"2661-8907"}],"subject":[],"published":{"date-parts":[[2025,7,22]]},"assertion":[{"value":"13 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No author has disclosed any conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"669"}}