{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,7]],"date-time":"2026-06-07T07:24:20Z","timestamp":1780817060753,"version":"3.54.1"},"reference-count":64,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T00:00:00Z","timestamp":1713225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Junta de Castilla y Le\u00f3n","award":["BU055P20 (JCyL\/FEDER, UE)"],"award-info":[{"award-number":["BU055P20 (JCyL\/FEDER, UE)"]}]},{"name":"Junta de Castilla y Le\u00f3n","award":["PID2020-119894GB-I00"],"award-info":[{"award-number":["PID2020-119894GB-I00"]}]},{"name":"Ministry of Science and Innovation","award":["BU055P20 (JCyL\/FEDER, UE)"],"award-info":[{"award-number":["BU055P20 (JCyL\/FEDER, UE)"]}]},{"name":"Ministry of Science and Innovation","award":["PID2020-119894GB-I00"],"award-info":[{"award-number":["PID2020-119894GB-I00"]}]},{"name":"Universidad de Burgos","award":["BU055P20 (JCyL\/FEDER, UE)"],"award-info":[{"award-number":["BU055P20 (JCyL\/FEDER, UE)"]}]},{"name":"Universidad de Burgos","award":["PID2020-119894GB-I00"],"award-info":[{"award-number":["PID2020-119894GB-I00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The most common preprocessing techniques used to deal with datasets having high dimensionality and a low number of instances\u2014or wide data\u2014are feature reduction (FR), feature selection (FS), and resampling. This study explores the use of FR and resampling techniques, expanding the limited comparisons between FR and filter FS methods in the existing literature, especially in the context of wide data. We compare the optimal outcomes from a previous comprehensive study of FS against new experiments conducted using FR methods. Two specific challenges associated with the use of FR are outlined in detail: finding FR methods that are compatible with wide data and the need for a reduction estimator of nonlinear approaches to process out-of-sample data. The experimental study compares 17 techniques, including supervised, unsupervised, linear, and nonlinear approaches, using 7 resampling strategies and 5 classifiers. The results demonstrate which configurations are optimal, according to their performance and computation time. Moreover, the best configuration\u2014namely, k Nearest Neighbor (KNN) + the Maximal Margin Criterion (MMC) feature reducer with no resampling\u2014is shown to outperform state-of-the-art algorithms.<\/jats:p>","DOI":"10.3390\/info15040223","type":"journal-article","created":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T07:54:36Z","timestamp":1713340476000},"page":"223","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An Extensive Performance Comparison between Feature Reduction and Feature Selection Preprocessing Algorithms on Imbalanced Wide Data"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0330-1605","authenticated-orcid":false,"given":"Ismael","family":"Ramos-P\u00e9rez","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Escuela Polit\u00e9cnica Superior, Universidad de Burgos, Avda. Cantabria s\/n, 09006 Burgos, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3269-0806","authenticated-orcid":false,"given":"Jos\u00e9 Antonio","family":"Barbero-Aparicio","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Escuela Polit\u00e9cnica Superior, Universidad de Burgos, Avda. Cantabria s\/n, 09006 Burgos, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0608-2743","authenticated-orcid":false,"given":"Antonio","family":"Canepa-Oneto","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Escuela Polit\u00e9cnica Superior, Universidad de Burgos, Avda. Cantabria s\/n, 09006 Burgos, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6965-0237","authenticated-orcid":false,"given":"\u00c1lvar","family":"Arnaiz-Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Escuela Polit\u00e9cnica Superior, Universidad de Burgos, Avda. Cantabria s\/n, 09006 Burgos, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8808-412X","authenticated-orcid":false,"given":"Jes\u00fas","family":"Maudes-Raedo","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Escuela Polit\u00e9cnica Superior, Universidad de Burgos, Avda. Cantabria s\/n, 09006 Burgos, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,16]]},"reference":[{"key":"ref_1","first-page":"272","article-title":"Artificial intelligence and machine learning in bioinformatics","volume":"1","author":"Lai","year":"2018","journal-title":"Encycl. Bioinform. Comput. Biol. ABC Bioinform."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e251","DOI":"10.7717\/peerj-cs.251","article-title":"RIdeogram: Drawing SVG graphics to visualize and map genome-wide data on the idiograms","volume":"6","author":"Hao","year":"2020","journal-title":"PeerJ Comput. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.ins.2021.01.020","article-title":"TAGA: Tabu Asexual Genetic Algorithm embedded in a filter\/filter feature selection approach for high-dimensional data","volume":"565","author":"Salesi","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_4","first-page":"314","article-title":"Curse of dimensionality","volume":"2017","author":"Keogh","year":"2017","journal-title":"Encycl. Mach. Learn. Data Min."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from imbalanced data","volume":"21","author":"He","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","article-title":"A review of feature selection techniques in bioinformatics","volume":"23","author":"Saeys","year":"2007","journal-title":"Bioinformatics"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.inffus.2020.01.005","article-title":"Overview and comparative study of dimensionality reduction techniques for high dimensional data","volume":"59","author":"Ayesha","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mohammed, R., Rawashdeh, J., and Abdullah, M. (2020, January 7\u20139). Machine learning with oversampling and undersampling techniques: Overview study and experimental results. Proceedings of the 2020 11th International Conference on Information and Communication Systems (ICICS), Irbid, Jordan.","DOI":"10.1109\/ICICS49469.2020.239556"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wijayanto, I., Humairani, A., Hadiyoso, S., Rizal, A., Prasanna, D.L., and Tripathi, S.L. (2023). Epileptic seizure detection on a compressed EEG signal using energy measurement. Biomed. Signal Process. Control, 85.","DOI":"10.1016\/j.bspc.2023.104872"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sachdeva, R.K., Bathla, P., Rani, P., Kukreja, V., and Ahuja, R. (2022, January 28\u201329). A Systematic Method for Breast Cancer Classification using RFE Feature Selection. Proceedings of the 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022, Greater Noida, India.","DOI":"10.1109\/ICACITE53722.2022.9823464"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"123866","DOI":"10.1016\/j.jclepro.2020.123866","article-title":"Evaluation and improvement of energy consumption prediction models using principal component analysis based feature reduction","volume":"279","author":"Parhizkar","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, W., Lu, L., and Wei, W. (2022). A Novel Supervised Filter Feature Selection Method Based on Gaussian Probability Density for Fault Diagnosis of Permanent Magnet DC Motors. Sensors, 22.","DOI":"10.3390\/s22197121"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.neucom.2018.07.038","article-title":"Fault diagnosis of rolling bearing based on feature reduction with global-local margin Fisher analysis","volume":"315","author":"Zhao","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_14","first-page":"491","article-title":"LDA and LSI as a dimensionality reduction method in arabic document classification","volume":"538","author":"Ayadi","year":"2015","journal-title":"Commun. Comput. Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pes, B. (2021). Learning from High-Dimensional and Class-Imbalanced Datasets Using Random Forests. Information, 12.","DOI":"10.3390\/info12080286"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"116015","DOI":"10.1016\/j.eswa.2021.116015","article-title":"When is resampling beneficial for feature selection with imbalanced wide data?","volume":"188","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mendes Junior, J.J.A., Freitas, M.L., Siqueira, H.V., Lazzaretti, A.E., Pichorim, S.F., and Stevan, S.L. (2020). Feature selection and dimensionality reduction: An extensive comparison in hand gesture classification by sEMG in eight channels armband approach. Biomed. Signal Process. Control, 59.","DOI":"10.1016\/j.bspc.2020.101920"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"125","DOI":"10.12928\/telkomnika.v9i1.678","article-title":"Appearance global and local structure fusion for face image recognition","volume":"9","author":"Muntasa","year":"2011","journal-title":"TELKOMNIKA (Telecommun. Comput. Electron. Control)"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yang, Y., Nie, F., Xiang, S., Zhuang, Y., and Wang, W. (2010, January 11\u201313). Local and global regressive mapping for manifold learning with out-of-sample extrapolation. Proceedings of the AAAI Conference on Artificial Intelligence, Atlanta, GA, USA.","DOI":"10.1609\/aaai.v24i1.7696"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/14786440109462720","article-title":"LIII. On lines and planes of closest fit to systems of points in space","volume":"2","author":"Pearson","year":"1901","journal-title":"Lond. Edinb. Dublin Philos. Mag. J. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1016\/j.patcog.2003.09.005","article-title":"Locality pursuit embedding","volume":"37","author":"Min","year":"2004","journal-title":"Pattern Recognit."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.neucom.2012.07.016","article-title":"Enhanced and parameterless Locality Preserving Projections for face recognition","volume":"99","author":"Dornaika","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_23","unstructured":"He, X., and Niyogi, P. (2003). Locality Preserving Projections. Adv. Neural Inf. Process. Syst., 16."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1162\/089976603321780317","article-title":"Laplacian eigenmaps for dimensionality reduction and data representation","volume":"15","author":"Belkin","year":"2003","journal-title":"Neural Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1016\/S0022-0000(03)00025-4","article-title":"Database-friendly random projections: Johnson-Lindenstrauss with binary coins","volume":"66","author":"Achlioptas","year":"2003","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","article-title":"The use of multiple measurements in taxonomic problems","volume":"7","author":"Fisher","year":"1936","journal-title":"Ann. Eugen."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1109\/TCBB.2014.2328334","article-title":"Gene selection using locality sensitive Laplacian score","volume":"11","author":"Liao","year":"2014","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_28","unstructured":"He, X., Cai, D., and Niyogi, P. (2005). Laplacian score for feature selection. Adv. Neural Inf. Process. Syst., 18."},{"key":"ref_29","first-page":"905","article-title":"Local fisher discriminant analysis for supervised dimensionality reduction","volume":"148","author":"Sugiyama","year":"2006","journal-title":"ACM Int. Conf. Proceeding Ser."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/TNN.2005.860852","article-title":"Efficient and robust feature extraction by maximum margin criterion","volume":"17","author":"Li","year":"2006","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2109","DOI":"10.1080\/03610920008832598","article-title":"SAVE: A method for dimension reduction and graphics in regression","volume":"29","year":"2000","journal-title":"Commun.-Stat.-Theory Methods"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2473","DOI":"10.1016\/j.sigpro.2007.03.006","article-title":"Gabor feature-based face recognition using supervised locality preserving projection","volume":"87","author":"Zheng","year":"2007","journal-title":"Signal Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/BF02289565","article-title":"Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis","volume":"29","author":"Kruskal","year":"1964","journal-title":"Psychometrika"},{"key":"ref_34","unstructured":"Borg, I., and Groenen, P.J. (2005). Modern Multidimensional Scaling: Theory and Applications, Springer."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","article-title":"Nonlinear dimensionality reduction by locally linear embedding","volume":"290","author":"Roweis","year":"2000","journal-title":"Science"},{"key":"ref_36","unstructured":"He, X., Cai, D., Yan, S., and Zhang, H.J. (2005, January 17\u201321). Neighborhood preserving embedding. Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV\u201905) Volume 1, Beijing, China."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yao, C., and Guo, Z. (2024, April 07). Revisit Neighborhood Preserving Embedding: A New Criterion for Measuring the Manifold Similarity in Dimension Reduction. Available online: https:\/\/ssrn.com\/abstract=4349051.","DOI":"10.2139\/ssrn.4349051"},{"key":"ref_38","unstructured":"Hinton, G.E., and Roweis, S. (2002). Stochastic neighbor embedding. Adv. Neural Inf. Process. Syst., 15."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1985). Learning Internal Representations by Error Propagation, California Univ San Diego La Jolla Inst for Cognitive Science. Technical Report.","DOI":"10.21236\/ADA164453"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"106839","DOI":"10.1016\/j.csda.2019.106839","article-title":"Benchmark for filter methods for feature selection in high-dimensional classification data","volume":"143","author":"Bommert","year":"2020","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","article-title":"Wrappers for feature subset selection","volume":"97","author":"Kohavi","year":"1997","journal-title":"Artif. Intell."},{"key":"ref_43","unstructured":"Lal, T.N., Chapelle, O., Weston, J., and Elisseeff, A. (2006). Feature Extraction: Foundations and Applications, Springer."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene selection for cancer classification using support vector machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.patcog.2019.02.023","article-title":"The impact of class imbalance in classification performance metrics based on the binary confusion matrix","volume":"91","author":"Luque","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_46","unstructured":"Japkowicz, N. (2000, January 13\u201315). The Class Imbalance Problem: Significance and Strategies. Proceedings of the 2000 International Conference on Artificial Intelligence (ICAI), Vancouver, BC, Canada."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1162\/089976698300017197","article-title":"Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms","volume":"10","author":"Dietterich","year":"1998","journal-title":"Neural Comput."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.knosys.2011.06.013","article-title":"On the effectiveness of preprocessing methods when dealing with different levels of class imbalance","volume":"25","author":"Mollineda","year":"2012","journal-title":"Knowl.-Based Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s00500-008-0319-7","article-title":"Evolutionary rule-based systems for imbalanced datasets","volume":"13","year":"2009","journal-title":"Soft Comput."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3236","DOI":"10.1016\/j.patcog.2007.02.007","article-title":"Markov blanket-embedded genetic algorithm for gene selection","volume":"40","author":"Zhu","year":"2007","journal-title":"Pattern Recognit."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3136625","article-title":"Feature selection: A data perspective","volume":"50","author":"Li","year":"2018","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Bol\u00f3n-Canedo, V., and Alonso-Betanzos, A. (2018). Recent Advances in Ensembles for Feature Selection, Springer.","DOI":"10.1007\/978-3-319-90080-3"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/TSMCC.2011.2161285","article-title":"A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches","volume":"42","author":"Galar","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.)"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/0005-2795(75)90109-9","article-title":"Comparison of the predicted and observed secondary structure of T4 phage lysozyme","volume":"405","author":"Matthews","year":"1975","journal-title":"Biochim. Biophys. Acta (BBA)-Protein Struct."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1093\/bioinformatics\/16.5.412","article-title":"Assessing the accuracy of prediction algorithms for classification: An overview","volume":"16","author":"Baldi","year":"2000","journal-title":"Bioinformatics"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Chicco, D., and Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom., 21.","DOI":"10.1186\/s12864-019-6413-7"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Chicco, D., and Jurman, G. (2023). The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification. Biodata Min., 16.","DOI":"10.1186\/s13040-023-00322-4"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_60","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple datasets","volume":"7","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_61","first-page":"2677","article-title":"An Extension on \u201cStatistical Comparisons of Classifiers over Multiple Data Sets\u201d for all Pairwise Comparisons","volume":"9","author":"Garcia","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_62","unstructured":"Benavoli, A., Corani, G., Mangili, F., Zaffalon, M., and Ruggeri, F. (2014;, January 22\u201324). A Bayesian Wilcoxon signed-rank test based on the Dirichlet process. Proceedings of the International Conference on Machine Learning, Beijing, China."},{"key":"ref_63","unstructured":"Kuncheva, L.I., Matthews, C.E., Arnaiz-Gonz\u00e1lez, A., and Rodr\u00edguez, J.J. (2020). Feature selection from high-dimensional data with very low sample size: A cautionary tale. arXiv."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","article-title":"A survey on semi-supervised learning","volume":"109","author":"Hoos","year":"2020","journal-title":"Mach. Learn."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/4\/223\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:28:53Z","timestamp":1760106533000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/4\/223"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,16]]},"references-count":64,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["info15040223"],"URL":"https:\/\/doi.org\/10.3390\/info15040223","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,16]]}}}