{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:11:51Z","timestamp":1753884711932,"version":"3.41.2"},"reference-count":47,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","funder":[{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61272194","61502060"],"award-info":[{"award-number":["61272194","61502060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"The Project of Chongqing Natural Science Foundation","award":["cstc2019jcyj-msxmX0683"],"award-info":[{"award-number":["cstc2019jcyj-msxmX0683"]}]},{"name":"The Science and Technology Project Affiliated with the Education Department of Chongqing Municipality","award":["CYB20063"],"award-info":[{"award-number":["CYB20063"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2024,3,15]]},"abstract":"<jats:p> The prototype reduction (PR) methods, as an important data pre-processing task, can improve instance-based classifiers by removing noise and\/or redundant samples. Recently, a series of PR methods with different heuristic strategies have been developed. Among them, clustering-based PR methods have shown competitive performance. Yet, they still suffer from the following issues: (a) most methods heavily rely on parameters; (b) most fail to remove suspicious noisy samples from the training set; (c) almost all fail to handle manifold data with nonspherical distributions effectively; (d) some have a relatively high time complexity. To advance the state of the art of clustering-based PR methods by overcoming the above issues, a novel heuristics PR method based on supervised local density peaks clustering (PRLDPC) is proposed. The main ideas of PRLDPC are concluded as follows: (a) a supervised local density peaks clustering (SLDPC) is first proposed to divide the training set into homogeneous and heterogeneous sub-clusters; (b) SLDPC-based edition method is second proposed to identify and remove noisy samples from heterogeneous sub-clusters; (c) an SLDPC-based condensing method is third proposed to obtain reduced samples from homogeneous sub-clusters and pruned heterogeneous sub-clusters. Intensive experiments have proven that (a) PRLDPC can outperform six state-of-the-art PR methods on extensive UCI and Kaggle data sets in weighing the reduction rate and classification accuracy of three instance-based classifiers; (b) PRLDPC is relatively fast and has a relatively low time complexity [Formula: see text]. <\/jats:p>","DOI":"10.1142\/s0218001424500022","type":"journal-article","created":{"date-parts":[[2024,1,27]],"date-time":"2024-01-27T04:00:15Z","timestamp":1706328015000},"source":"Crossref","is-referenced-by-count":0,"title":["PRLDPC: A Heuristics Prototype Reduction Method Based on Supervised Local Density Clustering for Instance-Based Classifiers"],"prefix":"10.1142","volume":"38","author":[{"given":"Xing","family":"Huang","sequence":"first","affiliation":[{"name":"School of Information Security, Chongqing College of Mobile Communication, Chongqing 401420, P. R. China"},{"name":"School of Artificial Intelligence and Big Data, Chongqing Industry Polytechnic College, Chongqing 401120, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1691-2644","authenticated-orcid":false,"given":"Junnan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Big Data, Chongqing Industry Polytechnic College, Chongqing 401120, P. R. China"}]}],"member":"219","published-online":{"date-parts":[[2024,3,20]]},"reference":[{"key":"S0218001424500022BIB001","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2018.2874017"},{"key":"S0218001424500022BIB002","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.06.053"},{"key":"S0218001424500022BIB003","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.12.046"},{"key":"S0218001424500022BIB004","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-021-03304-5"},{"key":"S0218001424500022BIB005","doi-asserted-by":"publisher","DOI":"10.1007\/s42235-022-00303-z"},{"key":"S0218001424500022BIB006","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-022-10280-8"},{"key":"S0218001424500022BIB007","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2014.07.020"},{"key":"S0218001424500022BIB008","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijnurstu.2019.07.001"},{"key":"S0218001424500022BIB009","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.11.005"},{"key":"S0218001424500022BIB010","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1968.1054155"},{"key":"S0218001424500022BIB011","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2012.07.007"},{"key":"S0218001424500022BIB012","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.11.139"},{"key":"S0218001424500022BIB013","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.08.028"},{"key":"S0218001424500022BIB014","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109060"},{"key":"S0218001424500022BIB015","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-01732-1"},{"key":"S0218001424500022BIB016","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-019-01598-y"},{"key":"S0218001424500022BIB017","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105804"},{"key":"S0218001424500022BIB018","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2020.2991306"},{"key":"S0218001424500022BIB019","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2005.11.004"},{"key":"S0218001424500022BIB020","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3027745"},{"key":"S0218001424500022BIB021","first-page":"997","volume":"9","author":"Marchiori E.","year":"2008","journal-title":"J. Mach. Learn. Res."},{"issue":"34","key":"S0218001424500022BIB022","first-page":"1","volume":"3","author":"Mohammadzadeh H.","year":"2020","journal-title":"Int. J. Commun. Syst."},{"key":"S0218001424500022BIB023","doi-asserted-by":"publisher","DOI":"10.1016\/S0031-3203(01)00208-4"},{"key":"S0218001424500022BIB024","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2010.08.014"},{"key":"S0218001424500022BIB025","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-008-0142-x"},{"key":"S0218001424500022BIB026","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-014-0393-7"},{"key":"S0218001424500022BIB027","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.09.031"},{"key":"S0218001424500022BIB028","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3150403"},{"key":"S0218001424500022BIB029","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2021.3120138"},{"key":"S0218001424500022BIB030","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.04.036"},{"key":"S0218001424500022BIB031","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-022-12409-x"},{"key":"S0218001424500022BIB032","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2022.3166546"},{"key":"S0218001424500022BIB033","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3055613"},{"key":"S0218001424500022BIB034","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.05.055"},{"key":"S0218001424500022BIB035","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.10.029"},{"key":"S0218001424500022BIB036","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109190"},{"key":"S0218001424500022BIB037","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2009.09.022"},{"key":"S0218001424500022BIB038","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2018.2842113"},{"key":"S0218001424500022BIB039","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2780094"},{"key":"S0218001424500022BIB040","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1972.4309137"},{"key":"S0218001424500022BIB041","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2013.2257480"},{"key":"S0218001424500022BIB042","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.12.040"},{"key":"S0218001424500022BIB043","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2018.05.029"},{"key":"S0218001424500022BIB044","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-019-03865-z"},{"key":"S0218001424500022BIB045","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2021.3114865"},{"issue":"5","key":"S0218001424500022BIB046","first-page":"2179","volume":"33","author":"Zhao H.","year":"2021","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"S0218001424500022BIB047","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2016.05.007"}],"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218001424500022","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T08:22:24Z","timestamp":1714983744000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S0218001424500022"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,15]]},"references-count":47,"journal-issue":{"issue":"03","published-print":{"date-parts":[[2024,3,15]]}},"alternative-id":["10.1142\/S0218001424500022"],"URL":"https:\/\/doi.org\/10.1142\/s0218001424500022","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"type":"print","value":"0218-0014"},{"type":"electronic","value":"1793-6381"}],"subject":[],"published":{"date-parts":[[2024,3,15]]},"article-number":"2450002"}}