{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T18:46:30Z","timestamp":1781808390282,"version":"3.54.5"},"reference-count":58,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T00:00:00Z","timestamp":1642118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020MF082"],"award-info":[{"award-number":["ZR2020MF082"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Collaborative Innovation Center for Intelligent Green Manufacturing Technology and Equipment of Shandong Province","award":["IGSD-2020-012"],"award-info":[{"award-number":["IGSD-2020-012"]}]},{"name":"Qingdao Top Talent Program of Entrepreneurship and Innovation","award":["19-3-2-11-zhc"],"award-info":[{"award-number":["19-3-2-11-zhc"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2018YFB1601500"],"award-info":[{"award-number":["2018YFB1601500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Aggressive driving behavior (ADB) is one of the main causes of traffic accidents. The accurate recognition of ADB is the premise to timely and effectively conduct warning or intervention to the driver. There are some disadvantages, such as high miss rate and low accuracy, in the previous data-driven recognition methods of ADB, which are caused by the problems such as the improper processing of the dataset with imbalanced class distribution and one single classifier utilized. Aiming to deal with these disadvantages, an ensemble learning-based recognition method of ADB is proposed in this paper. First, the majority class in the dataset is grouped employing the self-organizing map (SOM) and then are combined with the minority class to construct multiple class balance datasets. Second, three deep learning methods, including convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU), are employed to build the base classifiers for the class balance datasets. Finally, the ensemble classifiers are combined by the base classifiers according to 10 different rules, and then trained and verified using a multi-source naturalistic driving dataset acquired by the integrated experiment vehicle. The results suggest that in terms of the recognition of ADB, the ensemble learning method proposed in this research achieves better performance in accuracy, recall, and F1-score than the aforementioned typical deep learning methods. Among the ensemble classifiers, the one based on the LSTM and the Product Rule has the optimal performance, and the other one based on the LSTM and the Sum Rule has the suboptimal performance.<\/jats:p>","DOI":"10.3390\/s22020644","type":"journal-article","created":{"date-parts":[[2022,1,16]],"date-time":"2022-01-16T20:45:21Z","timestamp":1642365921000},"page":"644","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Recognition Method of Aggressive Driving Behavior Based on Ensemble Learning"],"prefix":"10.3390","volume":"22","author":[{"given":"Hanqing","family":"Wang","sequence":"first","affiliation":[{"name":"College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2418-7394","authenticated-orcid":false,"given":"Xiaoyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China"},{"name":"Collaborative Innovation Center for Intelligent Green Manufacturing Technology and Equipment of Shandong Province, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyan","family":"Han","sequence":"additional","affiliation":[{"name":"College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Xiang","sequence":"additional","affiliation":[{"name":"College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shangqing","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1023\/A:1007649804201","article-title":"Human Factors in the Causation of Road Traffic Crashes","volume":"16","author":"Petridou","year":"2000","journal-title":"Eur. J. Epidemiol."},{"key":"ref_2","unstructured":"(2021, May 22). Aggressive Driving Research Update, Available online: https:\/\/safety.fhwa.dot.gov\/speedmgt\/ref_mats\/fhwasa1304\/resources2\/38%20-%20Aggressive%20Driving%202009%20Research%20Update.pdf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.trf.2016.03.003","article-title":"Do Driver Anger and Aggression Contribute to the Odds of a Crash? A Population-Level Analysis","volume":"42","author":"Wickens","year":"2016","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Vahedi, J., Shariat Mohaymany, A., Tabibi, Z., and Mehdizadeh, M. (2018). Aberrant Driving Behaviour, Risk Involvement, and Their Related Factors Among Taxi Drivers. Int. J. Environ. Res. Public Health, 15.","DOI":"10.3390\/ijerph15081626"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/S0001-4575(03)00037-X","article-title":"Aggressive Driving: An Observational Study of Driver, Vehicle, and Situational Variables","volume":"36","author":"Shinar","year":"2004","journal-title":"Accid. Anal. Prev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1002\/(SICI)1098-2337(1999)25:6<409::AID-AB2>3.0.CO;2-0","article-title":"Traffic Congestion, Driver Stress, and Driver Aggression","volume":"25","author":"Hennessy","year":"1999","journal-title":"Aggress. Behav."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.trf.2016.02.010","article-title":"Aggression on the Road: Relationships between Dysfunctional Impulsivity, Forgiveness, Negative Emotions, and Aggressive Driving","volume":"42","author":"Lajunen","year":"2016","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/S1369-8478(99)00002-9","article-title":"Aggressive Driving: The Contribution of the Drivers and the Situation","volume":"1","author":"Shinar","year":"1998","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_9","unstructured":"(2021, June 15). A Review of the Literature on Aggressive Driving Research. Available online: https:\/\/www.stopandgo.org\/research\/aggressive\/tasca.pdf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1111\/j.1559-1816.2001.tb00204.x","article-title":"The Effects of Trait Driving Anger, Anonymity, and Aggressive Stimuli on Aggressive Driving Behavior","volume":"31","author":"Bell","year":"2001","journal-title":"J. Appl. Soc. Pyschol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1109\/TITS.2010.2050200","article-title":"Driving Safety Monitoring Using Semisupervised Learning on Time Series Data","volume":"11","author":"Wang","year":"2010","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.aap.2014.11.012","article-title":"Modeling Anger and Aggressive Driving Behavior in a Dynamic Choice\u2013Latent Variable Model","volume":"75","author":"Danaf","year":"2015","journal-title":"Accid. Anal. Prev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.aap.2017.08.017","article-title":"The Use of a Driving Simulator to Determine How Time Pressures Impact Driver Aggressiveness","volume":"108","author":"Fitzpatrick","year":"2017","journal-title":"Accid. Anal. Prev."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105709","DOI":"10.1016\/j.aap.2020.105709","article-title":"Measuring the Perception of Aggression in Driving Behavior","volume":"145","author":"Kerwin","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/j.trc.2020.02.028","article-title":"On-Line Aggressive Driving Identification Based on in-Vehicle Kinematic Parameters under Naturalistic Driving Conditions","volume":"114","author":"Ma","year":"2020","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.aap.2017.04.012","article-title":"Can Vehicle Longitudinal Jerk Be Used to Identify Aggressive Drivers? An Examination Using Naturalistic Driving Data","volume":"104","author":"Feng","year":"2017","journal-title":"Accid. Anal. Prev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1109\/MITS.2017.2666583","article-title":"Investigation of Route-Independent Aggressive and Safe Driving Features Obtained from Accelerometer Signals","volume":"9","author":"Zylius","year":"2017","journal-title":"IEEE Intell. Transport. Syst. Mag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8028","DOI":"10.1109\/ACCESS.2018.2889751","article-title":"A Comparative Study of Aggressive Driving Behavior Recognition Algorithms Based on Vehicle Motion Data","volume":"7","author":"Ma","year":"2019","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3377","DOI":"10.1109\/TITS.2019.2926639","article-title":"How Smartphone Accelerometers Reveal Aggressive Driving Behavior?\u2014The Key Is the Representation","volume":"21","author":"Carlos","year":"2020","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Moukafih, Y., Hafidi, H., and Ghogho, M. (2019, January 3\u20135). Aggressive driving detection using deep learning-based time series classification. Proceedings of the 2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), Sofia, Bulgaria.","DOI":"10.1109\/INISTA.2019.8778416"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Matousek, M., EL-Zohairy, M., Al-Momani, A., Kargl, F., and Bosch, C. (2019, January 9\u201312). Detecting anomalous driving behavior using neural networks. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.","DOI":"10.1109\/IVS.2019.8814246"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"113240","DOI":"10.1016\/j.eswa.2020.113240","article-title":"Driver Behavior Detection and Classification Using Deep Convolutional Neural Networks","volume":"149","author":"Shahverdy","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4957","DOI":"10.1109\/ACCESS.2020.3048915","article-title":"Driving Behavior Classification Based on Oversampled Signals of Smartphone Embedded Sensors Using an Optimized Stacked-LSTM Neural Networks","volume":"9","author":"Khodairy","year":"2021","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Carvalho Barbosa, R., Shoaib Ayub, M., Lopes Rosa, R., Zegarra Rodr\u00edguez, D., and Wuttisittikulkij, L. (2020). Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights. Sensors, 20.","DOI":"10.3390\/s20216218"},{"key":"ref_25","unstructured":"Silva, J.C., Saadi, M., Wuttisittikulkij, L., Militani, D.R., Rosa, R.L., Rodriguez, D.Z., and Otaibi, S.A. (2021). Light-Field Imaging Reconstruction Using Deep Learning Enabling Intelligent Autonomous Transportation System. IEEE Trans. Intell. Transport. Syst., 1\u20139."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ribeiro, D.A., Silva, J.C., Lopes Rosa, R., Saadi, M., Mumtaz, S., Wuttisittikulkij, L., Zegarra Rodr\u00edguez, D., and Al Otaibi, S. (2021). Light Field Image Quality Enhancement by a Lightweight Deformable Deep Learning Framework for Intelligent Transportation Systems. Electronics, 10.","DOI":"10.3390\/electronics10101136"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-009-9124-7","article-title":"Ensemble-Based Classifiers","volume":"33","author":"Rokach","year":"2010","journal-title":"Artif. Intell. Rev."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2130001","DOI":"10.1142\/S0129065721300011","article-title":"An Experimental Review on Deep Learning Architectures for Time Series Forecasting","volume":"31","author":"Riquelme","year":"2021","journal-title":"Int. J. Neur. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3358","DOI":"10.1016\/j.patcog.2007.04.009","article-title":"Cost-Sensitive Boosting for Classification of Imbalanced Data","volume":"40","author":"Sun","year":"2007","journal-title":"Pattern Recognit."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1109\/TSMCB.2008.2007853","article-title":"Exploratory Undersampling for Class-Imbalance Learning","volume":"39","author":"Liu","year":"2009","journal-title":"IEEE Trans. Syst. Man Cybern. B"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.patrec.2017.01.014","article-title":"Entropy-Based Matrix Learning Machine for Imbalanced Data Sets","volume":"88","author":"Zhu","year":"2017","journal-title":"Pattern Recognit. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, K., Xue, Q., Xing, Y., and Li, C. (2020). Improve Aggressive Driver Recognition Using Collision Surrogate Measurement and Imbalanced Class Boosting. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17072375"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e1249","DOI":"10.1002\/widm.1249","article-title":"Ensemble Learning: A Survey","volume":"8","author":"Sagi","year":"2018","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Saleh, K., Hossny, M., and Nahavandi, S. (2017, January 16\u201319). Driving behavior classification based on sensor data fusion using LSTM recurrent neural networks. Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317835"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, F., and Roggen, D. (2016). Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition. Sensors, 16.","DOI":"10.3390\/s16010115"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_38","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_39","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014, January 25\u201329). Learning phrase representations using rnn encoder\u2013decoder for statistical machine Translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Tsantekidis, A., Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M., and Iosifidis, A. (2017, January 24\u201327). Forecasting stock prices from the limit order book using convolutional neural networks. Proceedings of the 2017 IEEE 19th Conference on Business Informatics (CBI), Thessaloniki, Greece.","DOI":"10.1109\/CBI.2017.23"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"13949","DOI":"10.1109\/ACCESS.2018.2814818","article-title":"Convolutional Recurrent Deep Learning Model for Sentence Classification","volume":"6","author":"Hassan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Tian, Y., and Pan, L. (2015, January 19\u201321). Predicting short-term traffic flow by long short-term memory recurrent neural network. Proceedings of the 2015 IEEE International Conference on Smart City\/SocialCom\/SustainCom (SmartCity), Chengdu, China.","DOI":"10.1109\/SmartCity.2015.63"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/MCI.2018.2840738","article-title":"Recent Trends in Deep Learning Based Natural Language Processing","volume":"13","author":"Young","year":"2018","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.ejor.2017.11.054","article-title":"Deep Learning with Long Short-Term Memory Networks for Financial Market Predictions","volume":"270","author":"Fischer","year":"2018","journal-title":"Eur. J. Oper. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1016\/j.patcog.2014.11.014","article-title":"A Novel Ensemble Method for Classifying Imbalanced Data","volume":"48","author":"Sun","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1109\/5.58325","article-title":"The Self-Organizing Map","volume":"78","author":"Kohonen","year":"1990","journal-title":"Proc. IEEE"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Alanazi, S.A., Alruwaili, M., Ahmad, F., Alaerjan, A., and Alshammari, N. (2021). Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees. Sensors, 21.","DOI":"10.3390\/s21113760"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/j.ins.2018.12.007","article-title":"Spark-GHSOM: Growing Hierarchical Self-Organizing Map for Large Scale Mixed Attribute Datasets","volume":"496","author":"Malondkar","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"106872","DOI":"10.1016\/j.ecolind.2020.106872","article-title":"Water Quality Assessment of a River Catchment by the Composite Water Quality Index and Self-Organizing Maps","volume":"120","author":"Yotova","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Betti, A., Tucci, M., Crisostomi, E., Piazzi, A., Barmada, S., and Thomopulos, D. (2021). Fault Prediction and Early-Detection in Large PV Power Plants Based on Self-Organizing Maps. Sensors, 21.","DOI":"10.20944\/preprints202101.0632.v1"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TGRS.2016.2612821","article-title":"Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification","volume":"55","author":"Maggiori","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2538","DOI":"10.1109\/TCSVT.2017.2749620","article-title":"Adaptive Deep Convolutional Neural Networks for Scene-Specific Object Detection","volume":"29","author":"Li","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Koprinska, I., Wu, D., and Wang, Z. (2018, January 8\u201313). Convolutional neural networks for energy time series forecasting. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489399"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Kuo, P.-H., and Huang, C.-J. (2018). A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting. Energies, 11.","DOI":"10.3390\/en11010213"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Wei, D., Wang, B., Lin, G., Liu, D., Dong, Z., Liu, H., and Liu, Y. (2017). Research on Unstructured Text Data Mining and Fault Classification Based on RNN-LSTM with Malfunction Inspection Report. Energies, 10.","DOI":"10.3390\/en10030406"},{"key":"ref_56","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1109\/34.667881","article-title":"On Combining Classifiers","volume":"20","author":"Kittler","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/3-540-49257-7_15","article-title":"When is \u201cnearest neighbor\u201d meaningful?","volume":"1540","author":"Beeri","year":"1999","journal-title":"Database Theory\u2014ICDT\u201999"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/644\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:39:48Z","timestamp":1760362788000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/644"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,14]]},"references-count":58,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22020644"],"URL":"https:\/\/doi.org\/10.3390\/s22020644","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,14]]}}}