{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:16:40Z","timestamp":1760145400823,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100018693","name":"European Union\u2019s Horizon Europe research and innovation program","doi-asserted-by":"publisher","award":["101138678"],"award-info":[{"award-number":["101138678"]}],"id":[{"id":"10.13039\/100018693","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We live in the era of large data analysis, where processing vast datasets has become essential for uncovering valuable insights across various domains of our lives. Machine learning (ML) algorithms offer powerful tools for processing and analyzing this abundance of information. However, the considerable time and computational resources needed for training ML models pose significant challenges, especially within cascade schemes, due to the iterative nature of training algorithms, the complexity of feature extraction and transformation processes, and the large sizes of the datasets involved. This paper proposes a modification to the existing ML-based cascade scheme for analyzing large biomedical datasets by incorporating principal component analysis (PCA) at each level of the cascade. We selected the number of principal components to replace the initial inputs so that it ensured 95% variance retention. Furthermore, we enhanced the training and application algorithms and demonstrated the effectiveness of the modified cascade scheme through comparative analysis, which showcased a significant reduction in training time while improving the generalization properties of the method and the accuracy of the large data analysis. The improved enhanced generalization properties of the scheme stemmed from the reduction in nonsignificant independent attributes in the dataset, which further enhanced its performance in intelligent large data analysis.<\/jats:p>","DOI":"10.3390\/s24154762","type":"journal-article","created":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T07:57:23Z","timestamp":1721721443000},"page":"4762","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Method for Reducing Training Time of ML-Based Cascade Scheme for Large-Volume Data Analysis"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9761-0096","authenticated-orcid":false,"given":"Ivan","family":"Izonin","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK"},{"name":"Department of Artificial Intelligence, Lviv Polytechnic National University, 79013 Lviv, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3884-8280","authenticated-orcid":false,"given":"Roman","family":"Muzyka","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Lviv Polytechnic National University, 79013 Lviv, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9802-6799","authenticated-orcid":false,"given":"Roman","family":"Tkachenko","sequence":"additional","affiliation":[{"name":"Department of Publishing Information Technologies, Lviv Polytechnic National University, 79013 Lviv, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1667-2584","authenticated-orcid":false,"given":"Ivanna","family":"Dronyuk","sequence":"additional","affiliation":[{"name":"Faculty of Science & Technology, Jan Dlugosz University in Czestochowa, 42-200 Czestochowa, Poland"}]},{"given":"Kyrylo","family":"Yemets","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Lviv Polytechnic National University, 79013 Lviv, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7201-2703","authenticated-orcid":false,"given":"Stergios-Aristoteles","family":"Mitoulis","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"427","DOI":"10.11591\/eei.v12i1.4405","article-title":"The Effectiveness of Big Data Classification Control Based on Principal Component Analysis","volume":"12","author":"Mohammed","year":"2023","journal-title":"Bull. Electr. Eng. Inform."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1111\/coin.12289","article-title":"Using Visual Analytics to Develop Human and Machine-centric Models: A Review of Approaches and Proposed Information Technology","volume":"38","author":"Krak","year":"2022","journal-title":"Comput. Intell."},{"key":"ref_3","first-page":"1259","article-title":"A Systematic Review of Artificial Intelligence-Based Methods in Healthcare","volume":"12","author":"Apio","year":"2023","journal-title":"Int. J. Public Health"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"245","DOI":"10.15407\/pp2018.02.245","article-title":"The Practice Implementation of the Information Technology for Automated Definition of Semantic Terms Sets in the Content of Educational Materials","volume":"2139","author":"Krak","year":"2018","journal-title":"Probl. Program."},{"key":"ref_5","unstructured":"Manziuk, E., Barmak, O., Krak, I., and Mazurets, O. (2021, January 24\u201326). Formal Model of Trustworthy Artificial Intelligence Based on Standardization. Proceedings of the IntelITSIS\u20192021: 2nd International Workshop on Intelligent Information Technologies and Systems of Information Security, Khmelnytskyi, Ukraine."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1007\/978-3-031-16203-9_28","article-title":"Computational Intelligence in Medicine","volume":"Volume 149","author":"Babichev","year":"2023","journal-title":"Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chumachenko, D., Piletskiy, P., Sukhorukova, M., and Chumachenko, T. (2022). Predictive Model of Lyme Disease Epidemic Process Using Machine Learning Approach. Appl. Sci., 12.","DOI":"10.3390\/app12094282"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1016\/j.ijforecast.2022.07.007","article-title":"Tree-Based Heterogeneous Cascade Ensemble Model for Credit Scoring","volume":"39","author":"Liu","year":"2023","journal-title":"Int. J. Forecast."},{"key":"ref_9","first-page":"553","article-title":"Creditworthiness of Individual Borrowers Forecasting with Machine Learning Methods","volume":"Volume 159","author":"Hu","year":"2023","journal-title":"Advances in Artificial Systems for Medicine and Education VI"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"45","DOI":"10.2478\/jaiscr-2023-0006","article-title":"Fast Computational Approach to the Levenberg-Marquardt Algorithm for Training Feedforward Neural Networks","volume":"13","author":"Bilski","year":"2023","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"109744","DOI":"10.1016\/j.patcog.2023.109744","article-title":"Tri-Objective Optimization-Based Cascade Ensemble Pruning for Deep Forest","volume":"143","author":"Ji","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bisikalo, O.V., Kovtun, V.V., and Kovtun, O.V. (2020, January 16\u201318). Modeling of the Estimation of the Time to Failure of the Information System for Critical Use. Proceedings of the 2020 10th International Conference on Advanced Computer Information Technologies (ACIT), Deggendorf, Germany.","DOI":"10.1109\/ACIT49673.2020.9208883"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mochurad, L., and Shchur, G. (2021, January 5). Parallelization of Cryptographic Algorithm Based on Different Parallel Computing Technologies. Proceedings of the IT&AS\u20192021: Symposium on Information Technologies & Applied Sciences, Bratislava, Slovakia.","DOI":"10.23939\/istcmtm2021.02.005"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2302","DOI":"10.11591\/eei.v12i4.4711","article-title":"Feature-Based Real-Time Distributed Denial of Service Detection in SDN Using Machine Learning and Spark","volume":"12","author":"Samaan","year":"2023","journal-title":"Bull. Electr. Eng. Inform."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mochurad, L., Hladun, Y., Zasoba, Y., and Gregus, M. (2023). An Approach for Opening Doors with a Mobile Robot Using Machine Learning Methods. Big Data Cogn. Comput., 7.","DOI":"10.3390\/bdcc7020069"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3393","DOI":"10.11591\/eei.v10i6.3135","article-title":"An Effective Classification Approach for Big Data with Parallel Generalized Hebbian Algorithm","volume":"10","author":"Ali","year":"2021","journal-title":"Bull. Electr. Eng. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xu, S., Tang, Q., Jin, L., and Pan, Z. (2019). A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones. Sensors, 19.","DOI":"10.3390\/s19102307"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ganguli, C., Shandilya, S.K., Nehrey, M., and Havryliuk, M. (2023). Adaptive Artificial Bee Colony Algorithm for Nature-Inspired Cyber Defense. Systems, 11.","DOI":"10.3390\/systems11010027"},{"key":"ref_19","unstructured":"Shmelova, T., Sikirda, Y., Rizun, N., and Kucherov, D. (2019). Data Science Tools Application for Business Processes Modelling in Aviation: In Advances in Computer and Electrical Engineering, IGI Global."},{"key":"ref_20","first-page":"2579","article-title":"Visualizing Data Using T-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., and Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv.","DOI":"10.21105\/joss.00861"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1007\/978-3-319-91008-6_58","article-title":"Model and Principles for the Implementation of Neural-Like Structures Based on Geometric Data Transformations","volume":"Volume 754","author":"Hu","year":"2019","journal-title":"Advances in Computer Science for Engineering and Education"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"10009","DOI":"10.1109\/JSEN.2023.3257867","article-title":"Arbitrary Spatial Trajectory Reconstruction Based on a Single Inertial Sensor","volume":"23","author":"Wang","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"13398","DOI":"10.3934\/mbe.2023597","article-title":"A Non-Linear SVR-Based Cascade Model for Improving Prediction Accuracy of Biomedical Data Analysis","volume":"20","author":"Izonin","year":"2023","journal-title":"Math. Biosci. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.3390\/make4040055","article-title":"SGD-Based Cascade Scheme for Higher Degrees Wiener Polynomial Approximation of Large Biomedical Datasets","volume":"4","author":"Izonin","year":"2022","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"43","DOI":"10.21303\/2461-4262.2019.001005","article-title":"Optimal alternative selection models in a multi-stage decision-making process","volume":"6","author":"Mulesa","year":"2019","journal-title":"EUREKA Phys. Eng."},{"key":"ref_27","first-page":"361","article-title":"Development of Combined Information Technology for Time Series Prediction","volume":"Volume 689","author":"Shakhovska","year":"2018","journal-title":"Advances in Intelligent Systems and Computing II"},{"key":"ref_28","first-page":"1382","article-title":"Dimentionality Reduction Based on Binary Cooperative Particle Swarm Optimization","volume":"15","author":"Azmi","year":"2019","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1007\/978-3-030-20521-8_64","article-title":"SGD-Based Wiener Polynomial Approximation for Missing Data Recovery in Air Pollution Monitoring Dataset","volume":"Volume 11506","author":"Rojas","year":"2019","journal-title":"Advances in Computational Intelligence"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sambir, A., Yakovyna, V., and Seniv, M. (2017, January 20\u201323). Recruiting Software Architecture Using User Generated Data. Proceedings of the 2017 XIIIth International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH), Lviv, Ukraine.","DOI":"10.1109\/MEMSTECH.2017.7937557"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yakovyna, V., and Uhrynovskyi, B. (2020, January 23\u201326). User-Perceived Response Metrics in Android OS for Software Aging Detection. Proceedings of the 2020 IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT), Zbarazh, Ukraine.","DOI":"10.1109\/CSIT49958.2020.9322031"},{"key":"ref_32","unstructured":"(2024, March 04). CDC\u20142021 BRFSS Survey Data and Documentation, Available online: https:\/\/www.cdc.gov\/brfss\/annual_data\/annual_2021.html."},{"key":"ref_33","unstructured":"(2019, February 08). Sklearn.Linear_Model.SGDRegressor\u2014Scikit-Learn 0.20.2 Documentation. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.linear_model.SGDRegressor.html."},{"key":"ref_34","first-page":"430","article-title":"The Intellectual Structure of Sustainable Leadership Studies: Bibliometric Analysis","volume":"Volume 158","author":"Hu","year":"2023","journal-title":"Advances in Intelligent Systems, Computer Science and Digital Economics IV"},{"key":"ref_35","first-page":"10","article-title":"Entrepreneurial Competencies and Intentions among Students of Technical Universities","volume":"19","author":"Wasilczuk","year":"2021","journal-title":"Probl. Perspect. Manag."},{"key":"ref_36","first-page":"25","article-title":"Determination of the Best Microstructureand Titanium Alloy Powders Propertiesusing Neural Network","volume":"1","author":"Duriagina","year":"2018","journal-title":"J. Achiev. Mater. Manuf. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"100387","DOI":"10.1016\/j.crm.2021.100387","article-title":"Digital Technologies Can Enhance Climate Resilience of Critical Infrastructure","volume":"35","author":"Argyroudis","year":"2022","journal-title":"Clim. Risk Manag."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e16828","DOI":"10.1016\/j.heliyon.2023.e16828","article-title":"Analytical Method to Improve the Decision-Making Criteria Approach in Managing Digital Social Channels","volume":"9","author":"Fedushko","year":"2023","journal-title":"Heliyon"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/15\/4762\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:21:23Z","timestamp":1760109683000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/15\/4762"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,23]]},"references-count":38,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["s24154762"],"URL":"https:\/\/doi.org\/10.3390\/s24154762","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,7,23]]}}}