{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:39:49Z","timestamp":1771699189117,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T00:00:00Z","timestamp":1717113600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T00:00:00Z","timestamp":1717113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Spanish National Research Project","award":["PID2020-119478GB-I00"],"award-info":[{"award-number":["PID2020-119478GB-I00"]}]},{"DOI":"10.13039\/501100004359","name":"Swedish Research Council","doi-asserted-by":"crossref","award":["2016-05431"],"award-info":[{"award-number":["2016-05431"]}],"id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Differences in data size per class, also known as imbalanced data distribution, have become a common problem affecting data quality. Big Data scenarios pose a new challenge to traditional imbalanced classification algorithms, since they are not prepared to work with such amount of data. Split data strategies and lack of data in the minority class due to the use of MapReduce paradigm have posed new challenges for tackling the imbalance between classes in Big Data scenarios. Ensembles have been shown to be able to successfully address imbalanced data problems. Smart Data refers to data of enough quality to achieve high-performance models. The combination of ensembles and Smart Data, achieved through Big Data preprocessing, should be a great synergy. In this paper, we propose a novel Smart Data driven Decision Trees Ensemble methodology for addressing the imbalanced classification problem in Big Data domains, namely SD_DeTE methodology. This methodology is based on the learning of different decision trees using distributed quality data for the ensemble process. This quality data is achieved by fusing random discretization, principal components analysis, and clustering-based random oversampling for obtaining different Smart Data versions of the original data. Experiments carried out in 21 binary adapted datasets have shown that our methodology outperforms random forest.<\/jats:p>","DOI":"10.1007\/s12559-024-10295-z","type":"journal-article","created":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T05:01:36Z","timestamp":1717131696000},"page":"1572-1588","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Smart Data Driven Decision Trees Ensemble Methodology for Imbalanced Big Data"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1927-8673","authenticated-orcid":false,"given":"Diego","family":"Garc\u00eda-Gil","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Salvador","family":"Garc\u00eda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francisco","family":"Herrera","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,31]]},"reference":[{"key":"10295_CR1","doi-asserted-by":"crossref","unstructured":"Bansal M, Chana I, Clarke S. A survey on IoT Big data: Current status, 13 V\u2019s challenges, and future directions. ACM Comput Surv. 53(6).","DOI":"10.1145\/3419634"},{"key":"10295_CR2","doi-asserted-by":"crossref","unstructured":"Kaiser MS, Zenia N, Tabassum F, Mamun SA, Rahman MA, Islam MS, Mahmud M. 6G access network for intelligent Internet of Healthcare Things: Opportunity, challenges, and research directions. In: Kaiser MS, Bandyopadhyay A, Mahmud M, Ray K, editors. Proceedings of International Conference on Trends in Computational and Cognitive Engineering. Singapore: Springer Singapore; 2021. pp. 317\u201328.","DOI":"10.1007\/978-981-33-4673-4_25"},{"key":"10295_CR3","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.inffus.2017.10.001","volume":"42","author":"S Ram\u00edrez-Gallego","year":"2018","unstructured":"Ram\u00edrez-Gallego S, Fern\u00e1ndez A, Garc\u00eda S, Chen M, Herrera F. Big data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce. Inf Fusion. 2018;42:51\u201361.","journal-title":"Inf Fusion"},{"key":"10295_CR4","doi-asserted-by":"crossref","unstructured":"Khan N, Naim A, Hussain MR, Naveed QN, Ahmad N, Qamar S. The 51 v\u2019s of big data: Survey, technologies, characteristics, opportunities, issues and challenges. In: Proceedings of the International Conference on Omni-Layer Intelligent Systems, COINS \u201919. New York: Association for Computing Machinery; 2019. pp. 19\u201324.","DOI":"10.1145\/3312614.3312623"},{"key":"10295_CR5","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1016\/j.future.2018.04.053","volume":"87","author":"M Ge","year":"2018","unstructured":"Ge M, Bangui H, Buhnova B. Big data for Internet of Things: a survey. Futur Gener Comput Syst. 2018;87:601\u201314.","journal-title":"Futur Gener Comput Syst"},{"key":"10295_CR6","unstructured":"Shwartz-Ziv R, Armon A. Tabular data: Deep learning is not all you need. arXiv:2106.03253 [Preprint]. 2021. Available from: http:\/\/arxiv.org\/abs\/2106.03253."},{"key":"10295_CR7","doi-asserted-by":"crossref","unstructured":"Thabtah F, Hammoud S, Kamalov F, Gonsalves A. Data imbalance in classification: Experimental evaluation. Inf Sci. 2020;513:429\u201341.","DOI":"10.1016\/j.ins.2019.11.004"},{"key":"10295_CR8","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez A, Garc\u00eda S, Galar M, Prati RC, Krawczyk B, Herrera F. Learning from imbalanced data sets. Springer; 2018.","DOI":"10.1007\/978-3-319-98074-4"},{"key":"10295_CR9","doi-asserted-by":"crossref","unstructured":"Luengo J, Garc\u00eda-Gil D, Ram\u00edrez-Gallego S, Garc\u00eda S, Herrera F. Big data preprocessing - enabling smart data. Springer; 2020.","DOI":"10.1007\/978-3-030-39105-8"},{"issue":"2","key":"10295_CR10","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/s40747-017-0037-9","volume":"3","author":"A Fern\u00e1ndez","year":"2017","unstructured":"Fern\u00e1ndez A, del R\u00edo S, Chawla NV, Herrera F. An insight into imbalanced big data classification: Outcomes and challenges. Complex Intell Syst. 2017;3(2):105\u201320.","journal-title":"Complex Intell Syst"},{"key":"10295_CR11","doi-asserted-by":"crossref","unstructured":"Basgall MJ, Hasperu\u00e9 W, Naiouf M, Fern\u00e1ndez A, Herrera F. SMOTE-BD: an exact and scalable oversampling method for imbalanced classification in big data. J Comput Sci Technol. 2018;18(03).","DOI":"10.24215\/16666038.18.e23"},{"issue":"4","key":"10295_CR12","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/s13748-017-0128-2","volume":"6","author":"PD Guti\u00e9rrez","year":"2017","unstructured":"Guti\u00e9rrez PD, Lastra M, Ben\u00edtez JM, Herrera F. SMOTE-GPU: Big data preprocessing on commodity hardware for imbalanced classification. Progr Artif Intell. 2017;6(4):347\u201354.","journal-title":"Progr Artif Intell"},{"issue":"1","key":"10295_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-018-0151-6","volume":"5","author":"JL Leevy","year":"2018","unstructured":"Leevy JL, Khoshgoftaar TM, Bauder RA, Seliya N. A survey on addressing high-class imbalance in big data. J Big Data. 2018;5(1):1\u201330.","journal-title":"J Big Data"},{"key":"10295_CR14","doi-asserted-by":"crossref","unstructured":"Juez-Gil M, Arnaiz-Gonz\u00e1lez \u00c1, Rodr\u00edguez JJ, Garc\u00eda-Osorio C. Experimental evaluation of ensemble classifiers for imbalance in big data. Appl Soft Comput. 2021;108.","DOI":"10.1016\/j.asoc.2021.107447"},{"key":"10295_CR15","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.inffus.2021.05.017","volume":"76","author":"X Xie","year":"2021","unstructured":"Xie X, Zhang Q. An edge-cloud-aided incremental tensor-based fuzzy c-means approach with big data fusion for exploring smart data. Inf Fusion. 2021;76:168\u201374.","journal-title":"Inf Fusion"},{"issue":"4","key":"10295_CR16","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1109\/TSMCC.2011.2161285","volume":"42","author":"M Galar","year":"2012","unstructured":"Galar M, Fern\u00e1ndez A, Barrenechea E, Bustince H, Herrera F. A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches. IEEE Trans Syst Man Cybern C Appl Rev. 2012;42(4):463\u201384.","journal-title":"IEEE Trans Syst Man Cybern C Appl Rev"},{"key":"10295_CR17","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.ins.2018.12.002","volume":"479","author":"D Garc\u00eda-Gil","year":"2019","unstructured":"Garc\u00eda-Gil D, Luengo J, Garc\u00eda S, Herrera F. Enabling smart data: Noise filtering in big data classification. Inf Sci. 2019;479:135\u201352.","journal-title":"Inf Sci"},{"issue":"12","key":"10295_CR18","doi-asserted-by":"publisher","first-page":"3260","DOI":"10.1002\/int.22193","volume":"34","author":"D Garc\u00eda-Gil","year":"2019","unstructured":"Garc\u00eda-Gil D, Luque-S\u00e1nchez F, Luengo J, Garc\u00eda S, Herrera F. From big to smart data: Iterative ensemble filter for noise filtering in big data classification. Int J Intell Syst. 2019;34(12):3260\u201374.","journal-title":"Int J Intell Syst"},{"key":"10295_CR19","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.knosys.2018.03.012","volume":"150","author":"D Garc\u00eda-Gil","year":"2018","unstructured":"Garc\u00eda-Gil D, Ram\u00edrez-Gallego S, Garc\u00eda S, Herrera F. Principal components analysis random discretization ensemble for big data. Knowl Based Syst. 2018;150:166\u201374.","journal-title":"Knowl Based Syst"},{"issue":"34","key":"10295_CR20","first-page":"1","volume":"17","author":"X Meng","year":"2016","unstructured":"Meng X, Bradley J, Yavuz B, Sparks E, Venkataraman S, Liu D, Freeman J, Tsai D, Amde M, Owen S, Xin D, Xin R, Franklin MJ, Zadeh R, Zaharia M, Talwalkar A. MLlib: Machine learning in Apache Spark. J Mach Learn Res. 2016;17(34):1\u20137.","journal-title":"J Mach Learn Res"},{"key":"10295_CR21","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/978-3-319-59650-1_24","volume-title":"Hybrid artificial intelligent systems","author":"J Carrasco","year":"2017","unstructured":"Carrasco J, Garc\u00eda S, del Mar Rueda M. rNPBST: An R package covering non-parametric and Bayesian statistical tests. In: Mart\u00ednez de Pis\u00f3n FJ, Urraca R, Quinti\u00e1n H, Corchado E, editors. Hybrid artificial intelligent systems. Cham: Springer International Publishing; 2017. p. 281\u201392."},{"key":"10295_CR22","doi-asserted-by":"crossref","unstructured":"Wu X, Wen C, Wang Z, Liu W, Yang J. A novel ensemble-learning-based convolution neural network for handling imbalanced data. Cogn Comput. 2023;1\u201314.","DOI":"10.1007\/s12559-023-10187-8"},{"key":"10295_CR23","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.knosys.2018.05.037","volume":"158","author":"J Bi","year":"2018","unstructured":"Bi J, Zhang C. An empirical comparison on state-of-the-art multi-class imbalance learning algorithms and a new diversified ensemble learning scheme. Knowl Based Syst. 2018;158:81\u201393.","journal-title":"Knowl Based Syst"},{"issue":"1","key":"10295_CR24","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1145\/1007730.1007735","volume":"6","author":"GEAPA Batista","year":"2004","unstructured":"Batista GEAPA, Prati RC, Monard MC. A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor Newsl. 2004;6(1):20\u20139.","journal-title":"SIGKDD Explor Newsl"},{"key":"10295_CR25","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1016\/j.neucom.2021.08.086","volume":"464","author":"M Juez-Gil","year":"2021","unstructured":"Juez-Gil M, Arnaiz-Gonzalez A, Rodriguez JJ, Lopez-Nozal C, Garcia-Osorio C. Approx-SMOTE: Fast SMOTE for big data on Apache Spark. Neurocomputing. 2021;464:432\u20137.","journal-title":"Neurocomputing"},{"key":"10295_CR26","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1613\/jair.1.11192","volume":"61","author":"A Fern\u00e1ndez","year":"2018","unstructured":"Fern\u00e1ndez A, Garc\u00eda S, Herrera F, Chawla NV. SMOTE for learning from imbalanced data: Progress and challenges, marking the 15-year anniversary. J Artif Intell Res. 2018;61:863\u2013905.","journal-title":"J Artif Intell Res"},{"key":"10295_CR27","doi-asserted-by":"crossref","unstructured":"Ma J, Afolabi DO, Ren J, Zhen A. Predicting seminal quality via imbalanced learning with evolutionary safe-level synthetic minority over-sampling technique. Cogn Comput. 2019;1\u201312.","DOI":"10.1007\/s12559-019-09657-9"},{"key":"10295_CR28","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.neucom.2017.06.082","volume":"276","author":"S Nejatian","year":"2018","unstructured":"Nejatian S, Parvin H, Faraji E. Using sub-sampling and ensemble clustering techniques to improve performance of imbalanced classification. Neurocomputing. 2018;276:55\u201366.","journal-title":"Neurocomputing"},{"key":"10295_CR29","doi-asserted-by":"crossref","unstructured":"Le HL, Landa-Silva D, Galar M, Garcia S, Triguero I. EUSC: a clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification. Appl Soft Comput. 2021;101.","DOI":"10.1016\/j.asoc.2020.107033"},{"key":"10295_CR30","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.ins.2017.05.008","volume":"409","author":"W-C Lin","year":"2017","unstructured":"Lin W-C, Tsai C-F, Hu Y-H, Jhang J-S. Clustering-based undersampling in class-imbalanced data. Inf Sci. 2017;409:17\u201326.","journal-title":"Inf Sci"},{"key":"10295_CR31","doi-asserted-by":"crossref","unstructured":"Zhang Y-P, Zhang L-N, Wang Y-C. Cluster-based majority under-sampling approaches for class imbalance learning. In: 2010 2nd IEEE International Conference on Information and Financial Engineering. IEEE; 2010. pp. 400\u20134.","DOI":"10.1109\/ICIFE.2010.5609385"},{"issue":"3","key":"10295_CR32","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1016\/S0031-3203(02)00257-1","volume":"36","author":"R Barandela","year":"2003","unstructured":"Barandela R, S\u00e1nchez J, Garc\u00eda V, Rangel E. Strategies for learning in class imbalance problems. Pattern Recogn. 2003;36(3):849\u201351.","journal-title":"Pattern Recogn"},{"issue":"3","key":"10295_CR33","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1109\/TKDE.2005.50","volume":"17","author":"J Huang","year":"2005","unstructured":"Huang J, Ling CX. Using AUC and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng. 2005;17(3):299\u2013310.","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"8","key":"10295_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3554729","volume":"55","author":"T Yang","year":"2022","unstructured":"Yang T, Ying Y. AUC maximization in the era of big data and AI: a survey. ACM Comput Surv. 2022;55(8):1\u201337.","journal-title":"ACM Comput Surv"},{"key":"10295_CR35","unstructured":"Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters. In: Proceedings of the 6th Conference on Symposium on Operating Systems Design & Implementation - Volume 6, OSDI\u201904, USENIX Association, USA. 2004. p. 10."},{"key":"10295_CR36","unstructured":"White T. Hadoop: the definitive guide. O\u2019Reilly Media, Inc.; 2012."},{"issue":"1","key":"10295_CR37","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1089\/big.2012.1501","volume":"1","author":"J Lin","year":"2013","unstructured":"Lin J. MapReduce is good enough? If all you have is a hammer, throw away everything that\u2019s not a nail! Big Data. 2013;1(1):28\u201337.","journal-title":"Big Data"},{"key":"10295_CR38","unstructured":"Hamstra M, Karau H, Zaharia M, Konwinski A, Wendell P. Learning spark: Lightning-fast big data analytics. O\u2019Reilly Media; 2015."},{"key":"10295_CR39","unstructured":"Zaharia M, Chowdhury M, Das T, Dave A, Ma J, McCauly M, Franklin MJ, Shenker S, Stoica I. Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12). San Jose: USENIX; 2012. pp. 15\u201328."},{"issue":"1","key":"10295_CR40","first-page":"1235","volume":"17","author":"X Meng","year":"2016","unstructured":"Meng X, Bradley J, Yavuz B, Sparks E, Venkataraman S, Liu D, Freeman J, Tsai D, Amde M, Owen S, et al. MLlib: Machine learning in Apache Spark. J Mach Learn Res. 2016;17(1):1235\u201341.","journal-title":"J Mach Learn Res."},{"key":"10295_CR41","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.ins.2014.03.043","volume":"285","author":"S del R\u00edo","year":"2014","unstructured":"del R\u00edo S, L\u00f3pez V, Ben\u00edtez JM, Herrera F. On the use of MapReduce for imbalanced big data using random forest. Inf Sci. 2014;285:112\u201337.","journal-title":"Inf. Sci."},{"key":"10295_CR42","doi-asserted-by":"publisher","first-page":"106598","DOI":"10.1016\/j.knosys.2020.106598","volume":"212","author":"WC Sleeman IV","year":"2021","unstructured":"Sleeman WC IV, Krawczyk B. Multi-class imbalanced big data classification on spark. Knowl Based Syst. 2021;212:106598.","journal-title":"Knowl Based Syst"},{"issue":"3","key":"10295_CR43","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1007\/s13042-015-0478-7","volume":"8","author":"J Zhai","year":"2017","unstructured":"Zhai J, Zhang S, Wang C. The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers. Int J Mach Learn Cybern. 2017;8(3):1009\u201317.","journal-title":"Int J Mach Learn Cybern"},{"key":"10295_CR44","doi-asserted-by":"crossref","unstructured":"Ahmad A, Brown G. Random projection random discretization ensembles-ensembles of linear multivariate decision trees. IEEE Trans Knowl Data Eng. 2014;26(5):1225\u201339.","DOI":"10.1109\/TKDE.2013.134"},{"key":"10295_CR45","unstructured":"Steinbach M, Karypis G, Kumar V, et\u00a0al. A comparison of document clustering techniques. In: KDD Workshop on Text Mining, Vol. 400, Boston. 2000. pp. 525\u20136."},{"key":"10295_CR46","doi-asserted-by":"publisher","DOI":"10.1142\/9097","volume-title":"Data mining with decision trees: Theory and applications","author":"L Rokach","year":"2014","unstructured":"Rokach L, Maimon O. Data mining with decision trees: Theory and applications. 2nd ed. USA: World Scientific Publishing Co. Inc.; 2014.","edition":"2"},{"key":"10295_CR47","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"},{"key":"10295_CR48","unstructured":"Dua D, Graff C. UCI machine learning repository. 2017. http:\/\/archive.ics.uci.edu\/ml."},{"key":"10295_CR49","doi-asserted-by":"crossref","unstructured":"Triguero I, del R\u00edo S, L\u00f3pez V, Bacardit J, Ben\u00edtez JM, Herrera F. ROSEFW-RF: the winner algorithm for the ECBDL\u201914 big data competition: an extremely imbalanced big data bioinformatics problem. Knowl Based Syst. 2015;87:69\u201379.","DOI":"10.1016\/j.knosys.2015.05.027"},{"issue":"1","key":"10295_CR50","first-page":"80","volume":"1","author":"FI Adiba","year":"2020","unstructured":"Adiba FI, Islam T, Kaiser MS, Mahmud M, Rahman MA. Effect of corpora on classification of fake news using Naive Bayes classifier. Int J Autom Artif Intell Mach Learn. 2020;1(1):80\u201392.","journal-title":"Int J Autom Artif Intell Mach Learn"},{"issue":"1","key":"10295_CR51","first-page":"2653","volume":"18","author":"A Benavoli","year":"2017","unstructured":"Benavoli A, Corani G, Dem\u0161ar J, Zaffalon M. Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis. J Mach Learn Res. 2017;18(1):2653\u201388.","journal-title":"J Mach Learn Res"},{"key":"10295_CR52","doi-asserted-by":"crossref","unstructured":"Jagadeesan J, Kirupanithi DN, et\u00a0al., An optimized ensemble support vector machine-based extreme learning model for real-time big data analytics and disaster prediction. Cogn Comput. 2023;1\u201323.","DOI":"10.1007\/s12559-023-10176-x"},{"issue":"1","key":"10295_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40708-023-00193-9","volume":"10","author":"MA Rahman","year":"2023","unstructured":"Rahman MA, Brown DJ, Mahmud M, Harris M, Shopland N, Heym N, Sumich A, Turabee ZB, Standen B, Downes D, et al. Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning. Brain Inform. 2023;10(1):1\u201318.","journal-title":"Brain Inform"}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-024-10295-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12559-024-10295-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-024-10295-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T09:46:26Z","timestamp":1720172786000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12559-024-10295-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,31]]},"references-count":53,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["10295"],"URL":"https:\/\/doi.org\/10.1007\/s12559-024-10295-z","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"value":"1866-9956","type":"print"},{"value":"1866-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,31]]},"assertion":[{"value":"2 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}