{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T20:01:43Z","timestamp":1773518503576,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T00:00:00Z","timestamp":1680652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The field of automated machine learning (AutoML) has gained significant attention in recent years due to its ability to automate the process of building and optimizing machine learning models. However, the increasing amount of big data being generated has presented new challenges for AutoML systems in terms of big data management. In this paper, we introduce Fabolas and learning curve extrapolation as two methods for accelerating hyperparameter optimization. Four methods for quickening training were presented including Bag of Little Bootstraps, k-means clustering for Support Vector Machines, subsample size selection for gradient descent, and subsampling for logistic regression. Additionally, we also discuss the use of Markov Chain Monte Carlo (MCMC) methods and other stochastic optimization techniques to improve the efficiency of AutoML systems in managing big data. These methods enhance various facets of the training process, making it feasible to combine them in diverse ways to gain further speedups. We review several combinations that have potential and provide a comprehensive understanding of the current state of AutoML and its potential for managing big data in various industries. Furthermore, we also mention the importance of parallel computing and distributed systems to improve the scalability of the AutoML systems while working with big data.<\/jats:p>","DOI":"10.3390\/info14040223","type":"journal-article","created":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T05:42:44Z","timestamp":1680673364000},"page":"223","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["AutoML with Bayesian Optimizations for Big Data Management"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4632-6511","authenticated-orcid":false,"given":"Aristeidis","family":"Karras","sequence":"first","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4253-7661","authenticated-orcid":false,"given":"Christos","family":"Karras","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8490-6544","authenticated-orcid":false,"given":"Nikolaos","family":"Schizas","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8575-0358","authenticated-orcid":false,"given":"Markos","family":"Avlonitis","sequence":"additional","affiliation":[{"name":"Department of Informatics, Ionian University, 49100 Kerkira, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1825-5565","authenticated-orcid":false,"given":"Spyros","family":"Sioutas","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kang, J.S., Kang, J., Kim, J.J., Jeon, K.W., Chung, H.J., and Park, B.H. (2023). Neural Architecture Search Survey: A Computer Vision Perspective. Sensors, 23.","DOI":"10.3390\/s23031713"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.neucom.2021.12.014","article-title":"A review of neural architecture search","volume":"474","author":"Baymurzina","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_3","first-page":"9820","article-title":"Best Practices for Scientific Research on Neural Architecture Search","volume":"21","author":"Lindauer","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jin, H., Song, Q., and Hu, X. (2019, January 4\u20138). Auto-Keras: An Efficient Neural Architecture Search System. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD \u201919), Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330648"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1177\/1475921710388971","article-title":"Machine learning algorithms for damage detection under operational and environmental variability","volume":"10","author":"Figueiredo","year":"2011","journal-title":"Struct. Health Monit."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1109\/TII.2014.2349359","article-title":"Machine learning for predictive maintenance: A multiple classifier approach","volume":"11","author":"Susto","year":"2014","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.trc.2014.04.013","article-title":"Improving rail network velocity: A machine learning approach to predictive maintenance","volume":"45","author":"Li","year":"2014","journal-title":"Transp. Res. Part Emerg. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"St\u00fchler, E., Braune, S., Lionetto, F., Heer, Y., Jules, E., Westermann, C., Bergmann, A., and van H\u00f6vell, P. (2020). Framework for personalized prediction of treatment response in relapsing remitting multiple sclerosis. BMC Med. Res. Methodol., 20.","DOI":"10.1186\/s12874-020-0906-6"},{"key":"ref_9","first-page":"97","article-title":"How neural networks can help loan officers to make better informed application decisions","volume":"6","author":"Handzic","year":"2003","journal-title":"Informing Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1016\/j.eswa.2005.04.030","article-title":"Auto claim fraud detection using Bayesian learning neural networks","volume":"29","author":"Viaene","year":"2005","journal-title":"Expert Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"P\u00e9rez, J.M., Muguerza, J., Arbelaitz, O., Gurrutxaga, I., and Mart\u00edn, J.I. (2005, January 23\u201325). Consolidated tree classifier learning in a car insurance fraud detection domain with class imbalance. Proceedings of the International Conference on Pattern Recognition and Image Analysis, Bath, UK.","DOI":"10.1007\/11551188_41"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1007\/s10462-018-9637-z","article-title":"A survey of machine learning techniques for food sales prediction","volume":"52","author":"Tsoumakas","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Karras, C., Karras, A., Tsolis, D., Avlonitis, M., and Sioutas, S. (2022, January 17\u201320). A Hybrid Ensemble Deep Learning Approach for Emotion Classification. Proceedings of the 2022 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA.","DOI":"10.1109\/BigData55660.2022.10020483"},{"key":"ref_14","first-page":"6765","article-title":"Hyperband: A novel bandit-based approach to hyperparameter optimization","volume":"18","author":"Li","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Duan, J., Zeng, Z., Oprea, A., and Vasudevan, S. (2018, January 10\u201313). Automated generation and selection of interpretable features for enterprise security. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8621986"},{"key":"ref_16","unstructured":"Lee, D., Sugiyama, M., Luxburg, U., Guyon, I., and Garnett, R. (2016). Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_17","unstructured":"Zoph, B., and Le, Q.V. (2016). Neural architecture search with reinforcement learning. arXiv."},{"key":"ref_18","unstructured":"Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., and Hutter, F. (2015). Efficient and robust automated machine learning. Adv. Neural Inf. Process. Syst., 28."},{"key":"ref_19","unstructured":"Gaudel, R., and Sebag, M. (2010, January 21\u201325). Feature selection as a one-player game. Proceedings of the International Conference on Machine Learning, Haifa, Israel."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Katz, G., Shin, E.C.R., and Song, D. (2016, January 12\u201315). Explorekit: Automatic feature generation and selection. Proceedings of the 2016 IEEE 16th International Conference on Data Mining (ICDM), Barcelona, Spain.","DOI":"10.1109\/ICDM.2016.0123"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Nargesian, F., Samulowitz, H., Khurana, U., Khalil, E.B., and Turaga, D.S. (2017, January 19\u201325). Learning Feature Engineering for Classification. Proceedings of the IJCAI, Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/352"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kaul, A., Maheshwary, S., and Pudi, V. (2017, January 18\u201321). Autolearn\u2014Automated feature generation and selection. Proceedings of the 2017 IEEE International Conference on data mining (ICDM), New Orleans, LA, USA.","DOI":"10.1109\/ICDM.2017.31"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1111\/j.1467-9868.2010.00740.x","article-title":"Stability selection","volume":"72","author":"Meinshausen","year":"2010","journal-title":"J. R. Stat. Soc. Ser. (Stat. Methodol.)"},{"key":"ref_24","unstructured":"Pfahringer, B., Bensusan, H., and Giraud-Carrier, C.G. (July, January 29). Meta-Learning by Landmarking Various Learning Algorithms. Proceedings of the ICML, Stanford, CA, USA."},{"key":"ref_25","unstructured":"Klein, A., Falkner, S., Springenberg, J.T., and Hutter, F. (2017, January 24\u201326). Learning Curve Prediction with Bayesian Neural Networks. Proceedings of the ICLR, Toulon, France."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Eggensperger, K., Lindauer, M., and Hutter, F. (2017). Neural networks for predicting algorithm runtime distributions. arXiv.","DOI":"10.24963\/ijcai.2018\/200"},{"key":"ref_27","unstructured":"Brazdil, P.B., and Soares, C. (June, January 31). A comparison of ranking methods for classification algorithm selection. Proceedings of the European Conference on Machine Learning, Barcelona, Spain."},{"key":"ref_28","unstructured":"Andrychowicz, M., Denil, M., Gomez, S., Hoffman, M.W., Pfau, D., Schaul, T., Shillingford, B., and De Freitas, N. (2016, January 5\u201310). Learning to learn by gradient descent by gradient descent. Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Graves, A. (2012). Supervised Sequence Labelling with Recurrent Neural Networks, Springer.","DOI":"10.1007\/978-3-642-24797-2"},{"key":"ref_31","unstructured":"Chen, Y., Hoffman, M.W., Colmenarejo, S.G., Denil, M., Lillicrap, T.P., Botvinick, M., and Freitas, N. (2017, January 6\u201311). Learning to learn without gradient descent by gradient descent. Proceedings of the International Conference on Machine Learning, PMLR, Sydney, Australia."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_33","unstructured":"Elsken, T., Metzen, J.H., and Hutter, F. (2017). Simple and efficient architecture search for convolutional neural networks. arXiv."},{"key":"ref_34","unstructured":"Real, E., Moore, S., Selle, A., Saxena, S., Suematsu, Y.L., Tan, J., Le, Q.V., and Kurakin, A. (2017, January 6\u201311). Large-scale evolution of image classifiers. Proceedings of the International Conference on Machine Learning, PMLR, Sydney, Australia."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"He, Y., Lin, J., Liu, Z., Wang, H., Li, L.J., and Han, S. (2018, January 8\u201314). Amc: Automl for model compression and acceleration on mobile devices. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_48"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Guyon, I., Sun-Hosoya, L., Boull\u00e9, M., Escalante, H.J., Escalera, S., Liu, Z., Jajetic, D., Ray, B., Saeed, M., and Sebag, M. (2019). Analysis of the automl challenge series. Autom. Mach. Learn., 177\u2013219.","DOI":"10.1007\/978-3-030-05318-5_10"},{"key":"ref_37","unstructured":"Brochu, E., Cora, V.M., and De Freitas, N. (2010). A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hutter, F., Hoos, H.H., and Leyton-Brown, K. (2011, January 17\u201321). Sequential model-based optimization for general algorithm configuration. Proceedings of the International Conference on Learning and Intelligent Optimization, Rome, Italy.","DOI":"10.1007\/978-3-642-25566-3_40"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Feurer, M., Springenberg, J., and Hutter, F. (2015, January 25\u201330). Initializing Bayesian Hyperparameter Optimization via Meta-Learning. Proceedings of the AAAI Conference on Artificial Intelligence, Austin, TX, USA.","DOI":"10.1609\/aaai.v29i1.9354"},{"key":"ref_40","unstructured":"Jamieson, K., and Talwalkar, A. (2016, January 9\u201311). Non-stochastic best arm identification and hyperparameter optimization. Proceedings of the Artificial Intelligence and Statistics, PMLR, Cadiz, Spain."},{"key":"ref_41","unstructured":"Jaderberg, M., Dalibard, V., Osindero, S., Czarnecki, W.M., Donahue, J., Razavi, A., Vinyals, O., Green, T., Dunning, I., and Simonyan, K. (2017). Population based training of neural networks. arXiv."},{"key":"ref_42","unstructured":"Maclaurin, D., Duvenaud, D., and Adams, R. (2015, January 6\u201311). Gradient-based hyperparameter optimization through reversible learning. Proceedings of the International Conference on Machine Learning, PMLR, Lille, France."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zacharia, A., Zacharia, D., Karras, A., Karras, C., Giannoukou, I., Giotopoulos, K.C., and Sioutas, S. (2022, January 23\u201325). An Intelligent Microprocessor Integrating TinyML in Smart Hotels for Rapid Accident Prevention. Proceedings of the 2022 7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Ioannina, Greece.","DOI":"10.1109\/SEEDA-CECNSM57760.2022.9932982"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Schizas, N., Karras, A., Karras, C., and Sioutas, S. (2022). TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review. Future Internet, 14.","DOI":"10.3390\/fi14120363"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Nagarajah, T., and Poravi, G. (2019, January 29\u201331). A Review on Automated Machine Learning (AutoML) Systems. Proceedings of the 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), Bombay, India.","DOI":"10.1109\/I2CT45611.2019.9033810"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s41060-022-00309-0","article-title":"Automl: State of the art with a focus on anomaly detection, challenges, and research directions","volume":"14","author":"Bahri","year":"2022","journal-title":"Int. J. Data Sci. Anal."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"103375","DOI":"10.1016\/j.compbiomed.2019.103375","article-title":"A review of feature selection methods in medical applications","volume":"112","author":"Remeseiro","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Isabona, J., Imoize, A.L., and Kim, Y. (2022). Machine Learning-Based Boosted Regression Ensemble Combined with Hyperparameter Tuning for Optimal Adaptive Learning. Sensors, 22.","DOI":"10.3390\/s22103776"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., and Hassner, T. (2022, January 23\u201327). Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation. Proceedings of the Computer Vision\u2014ECCV 2022, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19818-2"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Li, Y., Shen, Y., Jiang, H., Zhang, W., Li, J., Liu, J., Zhang, C., and Cui, B. (2022). Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale. arXiv.","DOI":"10.14778\/3514061.3514071"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"104520","DOI":"10.1016\/j.chemolab.2022.104520","article-title":"A tutorial on automatic hyperparameter tuning of deep spectral modelling for regression and classification tasks","volume":"223","author":"Passos","year":"2022","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_52","unstructured":"Yu, T., and Zhu, H. (2020). Hyper-parameter optimization: A review of algorithms and applications. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"e1484","DOI":"10.1002\/widm.1484","article-title":"Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges","volume":"13","author":"Bischl","year":"2021","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Sipper, M. (2022). High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms. Algorithms, 15.","DOI":"10.3390\/a15090315"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Giotopoulos, K.C., Michalopoulos, D., Karras, A., Karras, C., and Sioutas, S. (2023). Modelling and Analysis of Neuro Fuzzy Employee Ranking System in the Public Sector. Algorithms, 16.","DOI":"10.3390\/a16030151"},{"key":"ref_56","first-page":"528","article-title":"Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets","volume":"Volume 54","author":"Singh","year":"2017","journal-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics"},{"key":"ref_57","unstructured":"Sch\u00f6n, S., Kermarrec, G., Kargoll, B., Neumann, I., Kosheleva, O., and Kreinovich, V. (2017). Econometrics for Financial Applications, Springer International Publishing."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Maglogiannis, I., Iliadis, L., Macintyre, J., and Cortez, P. (2022, January 17\u201320). An Overview of MCMC Methods: From Theory to Applications. Proceedings of the Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops, Crete, Greece.","DOI":"10.1007\/978-3-031-08341-9"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Karras, C., Karras, A., Tsolis, D., Giotopoulos, K.C., and Sioutas, S. (2022, January 23\u201325). Distributed Gibbs Sampling and LDA Modelling for Large Scale Big Data Management on PySpark. Proceedings of the 2022 7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Ioannina, Greece.","DOI":"10.1109\/SEEDA-CECNSM57760.2022.9932990"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Maglogiannis, I., Iliadis, L., Macintyre, J., and Cortez, P. (2022, January 17\u201320). Maximum Likelihood Estimators on MCMC Sampling Algorithms for Decision Making. Proceedings of the Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops, Crete, Greece.","DOI":"10.1007\/978-3-031-08333-4"},{"key":"ref_61","unstructured":"Swersky, K., Snoek, J., and Adams, R.P. (2013). Advances in Neural Information Processing Systems; NIPS\u201913, Curran Associates Inc."},{"key":"ref_62","unstructured":"Domhan, T., Springenberg, J.T., and Hutter, F. (2015, January 25\u201331). Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves. Proceedings of the 24th International Conference on Artificial Intelligence, Buenos Aires, Argentina."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1111\/rssb.12050","article-title":"A Scalable Bootstrap for Massive Data","volume":"76","author":"Kleiner","year":"2014","journal-title":"J. R. Stat. Soc. Ser. (Stat. Methodol.)"},{"key":"ref_64","first-page":"16","article-title":"Weighted bootstrap with probability in regression","volume":"Volume 8","author":"Norazan","year":"2009","journal-title":"WSEAS International Conference. Proceedings. Mathematics and Computers in Science and Engineering"},{"key":"ref_65","first-page":"1","article-title":"Resampling fewer than n observations: Gains, losses, and remedies for losses","volume":"7","author":"Bickel","year":"1997","journal-title":"Stat. Sin."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s10107-012-0572-5","article-title":"Sample size selection in optimization methods for machine learning","volume":"134","author":"Byrd","year":"2012","journal-title":"Math. Program."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1214\/14-AOS1220","article-title":"Local case-control sampling: Efficient subsampling in imbalanced data sets","volume":"42","author":"Fithian","year":"2014","journal-title":"Ann. Stat."},{"key":"ref_68","first-page":"1","article-title":"More efficient estimation for logistic regression with optimal subsamples","volume":"20","author":"Wang","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1080\/01621459.2017.1292914","article-title":"Optimal Subsampling for Large Sample Logistic Regression","volume":"113","author":"Wang","year":"2018","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_70","unstructured":"De Almeida, M.B., de P\u00e1dua Braga, A., and Braga, J.P. (2000, January 25). SVM-KM: Speeding SVMs learning with a priori cluster selection and k-means. Proceedings of the Vol. 1. Sixth Brazilian Symposium on Neural Networks, Rio de Janeiro, Brazil."},{"key":"ref_71","first-page":"175","article-title":"Support vector machine using K-means clustering","volume":"36","author":"Lee","year":"2007","journal-title":"J. Korean Stat. Soc."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2307","DOI":"10.1080\/03610918.2012.762388","article-title":"Weighted Support Vector Machine Using k-Means Clustering","volume":"43","author":"Bang","year":"2014","journal-title":"Commun. Stat.-Simul. Comput."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s11042-015-3058-7","article-title":"Dual-source discrimination power analysis for multi-instance contactless palmprint recognition","volume":"76","author":"Leng","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Leng, L., Li, M., and Teoh, A.B.J. (2013, January 16\u201318). Conjugate 2DPalmHash code for secure palm-print-vein verification. Proceedings of the 2013 6th International congress on image and signal processing (CISP), Hangzhou, China.","DOI":"10.1109\/CISP.2013.6743951"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2012.08.028","article-title":"Palmhash code vs. palmphasor code","volume":"108","author":"Leng","year":"2013","journal-title":"Neurocomputing"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/4\/223\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:10:28Z","timestamp":1760123428000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/4\/223"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,5]]},"references-count":75,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["info14040223"],"URL":"https:\/\/doi.org\/10.3390\/info14040223","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,5]]}}}