Challenges in KNN Classification

The KNN algorithm is one of the most popular data mining algorithms. It has been widely and successfully applied to data analysis applications across a variety of research topics in computer science. This paper illustrates that, despite its success, there remain many challenges in KNN classification...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on knowledge and data engineering Jg. 34; H. 10; S. 4663 - 4675
1. Verfasser:	Zhang, Shichao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Classification classification rule Data analysis Data mining KNN classification lazy learning Licenses Nearest neighbor methods Partitioning algorithms Prediction algorithms Training Training data
ISSN:	1041-4347, 1558-2191
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	The KNN algorithm is one of the most popular data mining algorithms. It has been widely and successfully applied to data analysis applications across a variety of research topics in computer science. This paper illustrates that, despite its success, there remain many challenges in KNN classification, including K computation, nearest neighbor selection, nearest neighbor search and classification rules. Having established these issues, recent approaches to their resolution are examined in more detail, thereby providing a potential roadmap for ongoing KNN-related research, as well as some new classification rules regarding how to tackle the issue of training sample imbalance. To evaluate the proposed approaches, some experiments were conducted with 15 UCI benchmark datasets.
AbstractList	The KNN algorithm is one of the most popular data mining algorithms. It has been widely and successfully applied to data analysis applications across a variety of research topics in computer science. This paper illustrates that, despite its success, there remain many challenges in KNN classification, including K computation, nearest neighbor selection, nearest neighbor search and classification rules. Having established these issues, recent approaches to their resolution are examined in more detail, thereby providing a potential roadmap for ongoing KNN-related research, as well as some new classification rules regarding how to tackle the issue of training sample imbalance. To evaluate the proposed approaches, some experiments were conducted with 15 UCI benchmark datasets.
Author	Zhang, Shichao
Author_xml	– sequence: 1 givenname: Shichao orcidid: 0000-0001-9981-2970 surname: Zhang fullname: Zhang, Shichao email: zhangsc@csu.edu.cn organization: College of Computer Science and Technology, Central South University, Changsha, PR China
BookMark	eNp9kLFOwzAQhi1UJNrCAyCWSMwpPtuN7RGFFlCrspTZcp0zuApJsdOBtyelFQMD093wf_-dvhEZNG2DhFwDnQBQfbdePMwmjDKYcCo0m9IzMoTpVOUMNAz6nQrIBRfygoxS2lJKlVQwJFn5busamzdMWWiyxWqVlbVNKfjgbBfa5pKce1snvDrNMXmdz9blU758eXwu75e547zocmGldUxaWRXWK1dZZJwLz4A5zSvuNqBASlCq8hJRWNQbqwsEJwsPhad8TG6PvbvYfu4xdWbb7mPTnzRMglBacc76lDymXGxTiuiNC93Pn120oTZAzUGHOegwBx3mpKMn4Q-5i-HDxq9_mZsjExDxN685CFpQ_g0XzWsa
CODEN	ITKEEH
CitedBy_id	crossref_primary_10_1145_3641857 crossref_primary_10_1063_5_0275606 crossref_primary_10_1109_TCSII_2024_3362957 crossref_primary_10_3390_math13142290 crossref_primary_10_1109_JSEN_2023_3322040 crossref_primary_10_14710_transmisi_27_3_140_148 crossref_primary_10_3390_pr11051460 crossref_primary_10_1007_s41748_025_00772_2 crossref_primary_10_1016_j_acags_2025_100288 crossref_primary_10_7717_peerj_cs_2247 crossref_primary_10_1016_j_ins_2024_121853 crossref_primary_10_1016_j_tifs_2025_105145 crossref_primary_10_3390_s24134256 crossref_primary_10_3390_fi17030107 crossref_primary_10_1002_cpe_70180 crossref_primary_10_1016_j_infsof_2024_107657 crossref_primary_10_1007_s11227_024_06539_2 crossref_primary_10_3390_cancers14225608 crossref_primary_10_1109_ACCESS_2024_3422313 crossref_primary_10_1016_j_knosys_2025_113886 crossref_primary_10_3390_app15094920 crossref_primary_10_1145_3653719 crossref_primary_10_1016_j_jconhyd_2025_104498 crossref_primary_10_1016_j_measurement_2025_117598 crossref_primary_10_3390_en17215424 crossref_primary_10_3389_fnbot_2023_1181864 crossref_primary_10_1109_JBHI_2024_3415641 crossref_primary_10_1145_3643645 crossref_primary_10_1007_s10928_025_09972_9 crossref_primary_10_1038_s41598_025_85960_w crossref_primary_10_1093_cercor_bhae498 crossref_primary_10_3390_healthcare11010081 crossref_primary_10_3390_iot6010001 crossref_primary_10_1016_j_ijar_2023_108966 crossref_primary_10_1007_s00521_024_10867_y crossref_primary_10_1016_j_nxener_2025_100350 crossref_primary_10_1109_JETCAS_2023_3252667 crossref_primary_10_3390_s24237710 crossref_primary_10_1016_j_inffus_2025_103186 crossref_primary_10_1016_j_techfore_2023_123008 crossref_primary_10_3354_meps14873 crossref_primary_10_3390_make7020027 crossref_primary_10_1016_j_pdisas_2024_100398 crossref_primary_10_3390_insects15060418 crossref_primary_10_1155_2021_1102083 crossref_primary_10_3390_jmse12111943 crossref_primary_10_1002_cpe_8289 crossref_primary_10_3389_fcomp_2024_1477501 crossref_primary_10_7717_peerj_17748 crossref_primary_10_1109_ACCESS_2024_3487841 crossref_primary_10_1007_s40747_025_02014_4 crossref_primary_10_1007_s00477_025_03020_1 crossref_primary_10_3390_app15116227 crossref_primary_10_1145_3733604 crossref_primary_10_7717_peerj_cs_2576 crossref_primary_10_1016_j_datak_2025_102464 crossref_primary_10_1007_s40031_025_01246_9 crossref_primary_10_1016_j_matlet_2024_137872 crossref_primary_10_1155_2023_6536768 crossref_primary_10_1016_j_ijleo_2025_172390 crossref_primary_10_1039_D5CC01291K crossref_primary_10_1016_j_infrared_2025_105713 crossref_primary_10_1016_j_asoc_2025_113070 crossref_primary_10_1016_j_knosys_2024_112635 crossref_primary_10_1007_s11760_024_03562_x crossref_primary_10_1016_j_segan_2025_101639 crossref_primary_10_1109_TITS_2024_3510678 crossref_primary_10_1007_s10115_025_02404_7 crossref_primary_10_1016_j_jmrt_2025_09_205 crossref_primary_10_1016_j_physleta_2025_130763 crossref_primary_10_1016_j_asr_2025_08_061 crossref_primary_10_1007_s11571_021_09735_5 crossref_primary_10_1016_j_teler_2024_100120 crossref_primary_10_1007_s11340_021_00787_6 crossref_primary_10_1016_j_inffus_2023_102145 crossref_primary_10_3390_rs16234414 crossref_primary_10_1016_j_bios_2024_116718 crossref_primary_10_1145_3656345 crossref_primary_10_1155_2022_7611670 crossref_primary_10_1016_j_aei_2024_102963 crossref_primary_10_1007_s43503_025_00066_6 crossref_primary_10_1016_j_aeue_2023_154999 crossref_primary_10_1088_1402_4896_ad88ba crossref_primary_10_1007_s00170_025_15473_3 crossref_primary_10_3390_chemistry7030080 crossref_primary_10_1016_j_kjs_2024_100324 crossref_primary_10_1109_TSUSC_2024_3390003 crossref_primary_10_1016_j_foodchem_2025_144279 crossref_primary_10_1016_j_saa_2025_125806 crossref_primary_10_1007_s11668_023_01794_6 crossref_primary_10_1142_S0218126625503360 crossref_primary_10_1016_j_fbio_2024_104003 crossref_primary_10_3390_s24227316 crossref_primary_10_1109_TEVC_2024_3418858 crossref_primary_10_1080_00222348_2025_2468610 crossref_primary_10_1109_TIM_2025_3602537 crossref_primary_10_2298_TSCI231111008C crossref_primary_10_1016_j_vlsi_2025_102515 crossref_primary_10_3390_agronomy14040719 crossref_primary_10_1145_3736404 crossref_primary_10_3390_computers14020073 crossref_primary_10_1371_journal_pone_0331089 crossref_primary_10_1109_TKDE_2021_3095103 crossref_primary_10_3390_su17177602 crossref_primary_10_3390_s25113280 crossref_primary_10_3233_AIC_230312 crossref_primary_10_3390_sym16070850 crossref_primary_10_1186_s12913_025_12502_5 crossref_primary_10_1088_2057_1976_adee28 crossref_primary_10_1109_TPAMI_2024_3400281 crossref_primary_10_1007_s10661_024_13209_6 crossref_primary_10_1145_3663575 crossref_primary_10_1016_j_jreng_2024_12_002 crossref_primary_10_1016_j_atech_2023_100343 crossref_primary_10_1016_j_foodcont_2025_111176 crossref_primary_10_1016_j_fuel_2024_132406 crossref_primary_10_3390_electronics12143002 crossref_primary_10_1016_j_psep_2024_11_135 crossref_primary_10_1145_3708353 crossref_primary_10_1007_s10472_023_09882_x crossref_primary_10_1016_j_buildenv_2024_111799 crossref_primary_10_1007_s11356_024_35026_7 crossref_primary_10_1109_JSEN_2023_3323674 crossref_primary_10_3390_math12040536 crossref_primary_10_1016_j_asoc_2025_113852 crossref_primary_10_1016_j_procs_2025_04_160 crossref_primary_10_1007_s00530_025_01676_x crossref_primary_10_3390_bdcc9040102 crossref_primary_10_1016_j_pce_2025_104093 crossref_primary_10_1016_j_knosys_2024_112357 crossref_primary_10_3390_app15137580 crossref_primary_10_3390_met14080856 crossref_primary_10_3390_s23062908
Cites_doi	10.1109/TGRS.2005.848706 10.1016/j.knosys.2019.01.016 10.1016/j.neucom.2020.03.016 10.1109/TSMC.2018.2790914 10.1109/TKDE.2017.2763618 10.1109/34.993558 10.1109/TPAMI.2010.57 10.1109/TPAMI.2002.1033219 10.1007/s00521-018-3836-z 10.1016/j.patcog.2016.11.010 10.1016/j.patrec.2017.09.036 10.1214/aoms/1177700079 10.1109/TPAMI.2019.2907086 10.1016/j.patcog.2018.08.003 10.1016/j.cag.2016.03.006 10.2307/1403797 10.1016/j.jss.2012.05.073 10.1109/TPAMI.2007.1140 10.1016/j.is.2016.03.003 10.1023/B:VISI.0000046589.39864.ee 10.1109/TPAMI.2006.126 10.3390/info9090234 10.1016/j.neucom.2015.12.035 10.1109/TKDE.2019.2909204 10.1109/TSMCB.2007.908363 10.1109/CVPR.2005.320 10.1016/j.patrec.2018.03.023 10.1109/TPAMI.2009.55 10.7551/mitpress/1120.003.0090 10.1007/BF00994658 10.1155/2010/597373 10.1007/s10115-007-0114-2 10.1016/j.eswa.2018.08.021 10.1111/j.1469-1809.1936.tb02137.x 10.1007/978-3-540-79299-4_7 10.1016/j.patcog.2019.106970 10.1109/TIT.1967.1053964 10.1145/2990508 10.1109/CVPR.2001.990456 10.1145/7902.7906 10.1007/BF00114779 10.1198/016214501750332839 10.1109/TNNLS.2017.2673241 10.1109/TPAMI.2007.1182 10.1007/978-3-540-39964-3_62 10.1109/TPAMI.2004.1273978 10.1088/1742-6596/978/1/012047 10.1109/TPAMI.2013.83 10.1007/978-3-642-39787-5_10 10.1016/j.patcog.2005.08.016 10.1016/j.patcog.2017.09.038 10.1007/978-3-540-74976-9_25 10.1007/s10489-009-0207-6 10.1016/j.patcog.2006.12.019 10.1016/j.patcog.2018.04.024 10.1007/978-3-642-10844-0_50 10.1016/j.neucom.2018.11.101 10.1016/S0167-8655(97)00112-8 10.1109/ACCESS.2019.2909752 10.1145/3319532 10.1007/s11704-018-7049-5 10.1145/1273496.1273523 10.1109/CVPR.2007.383124 10.1109/TKDE.2018.2858782 10.1016/0169-7439(87)80084-9 10.1007/3-540-48508-2_2 10.1109/TKDE.2016.2535287 10.14778/3397230.3397240 10.1109/TKDE.2018.2873378
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
DBID	97E ESBDL RIA RIE AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D
DOI	10.1109/TKDE.2021.3049250
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE/IET Electronic Library (IEL) (UW System Shared) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional
DatabaseTitle	CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional
DatabaseTitleList	Technology Research Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE/IET Electronic Library (IEL) (UW System Shared) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Computer Science
EISSN	1558-2191
EndPage	4675
ExternalDocumentID	10_1109_TKDE_2021_3049250 9314060
Genre	orig-research
GrantInformation_xml	– fundername: National Natural Science Foundation of China; Natural Science Foundation of China grantid: 61836016; 61672177 funderid: 10.13039/501100001809
GroupedDBID	-~X .DC 0R~ 29I 4.4 5GY 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACIWK AENEX AGQYO AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD ESBDL F5P HZ~ IEDLZ IFIPE IPLJI JAVBF LAI M43 MS~ O9- OCL P2P PQQKQ RIA RIE RNS RXW TAE TN5 UHB AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c336t-4a7ac27a7d6af8cdae2334f212c93d3cb18177188df7ee4ae9ba96e1c76f16f03
IEDL.DBID	RIE
ISICitedReferencesCount	184
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000853844700008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1041-4347
IngestDate	Sun Nov 09 07:07:01 EST 2025 Sat Nov 29 02:36:03 EST 2025 Tue Nov 18 22:11:36 EST 2025 Wed Aug 27 02:18:59 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	10
Language	English
License	https://creativecommons.org/licenses/by/4.0/legalcode
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c336t-4a7ac27a7d6af8cdae2334f212c93d3cb18177188df7ee4ae9ba96e1c76f16f03
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-9981-2970
OpenAccessLink	https://ieeexplore.ieee.org/document/9314060
PQID	2714898332
PQPubID	85438
PageCount	13
ParticipantIDs	crossref_citationtrail_10_1109_TKDE_2021_3049250 crossref_primary_10_1109_TKDE_2021_3049250 ieee_primary_9314060 proquest_journals_2714898332
PublicationCentury	2000
PublicationDate	2022-10-01
PublicationDateYYYYMMDD	2022-10-01
PublicationDate_xml	– month: 10 year: 2022 text: 2022-10-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on knowledge and data engineering
PublicationTitleAbbrev	TKDE
PublicationYear	2022
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref57 Dasgupta (ref79) ref12 ref56 ref15 ref59 ref14 ref58 ref53 Singh (ref20) ref52 ref11 ref55 ref10 ref54 Globerson (ref66); 18 ref17 Sebestyen (ref18) 1962 Hastie (ref19) ref51 ref50 Simard (ref43) ref46 ref45 ref48 Vincent (ref37) ref47 ref42 ref41 ref44 Zhang (ref16) 2008; 9 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref82 ref40 ref80 ref35 ref34 ref78 ref36 ref31 ref75 ref30 ref74 ref33 ref77 ref32 ref76 ref2 ref1 ref39 ref38 Tao (ref24) ref71 ref70 Zhang (ref81) 2010; 11 ref73 ref72 Goldberger (ref62); 17 ref68 ref23 ref67 ref26 ref25 ref69 ref64 ref63 ref22 ref21 ref65 ref28 ref27 ref29 ref60 Weinberger (ref61); 18
References_xml	– start-page: 409 volume-title: Proc. 8th Int. Conf. Neural Inf. Process. Syst. ident: ref19 article-title: Discriminant adaptive nearest neighbor classification and regression – ident: ref25 doi: 10.1109/TGRS.2005.848706 – ident: ref30 doi: 10.1016/j.knosys.2019.01.016 – ident: ref72 doi: 10.1016/j.neucom.2020.03.016 – ident: ref56 doi: 10.1109/TSMC.2018.2790914 – ident: ref14 doi: 10.1109/TKDE.2017.2763618 – volume-title: Decision-Making Processes in Pattern Recognition year: 1962 ident: ref18 – ident: ref5 article-title: 30 questions to test a data scientist on k-nearest neighbors (kNN) algorithm – ident: ref44 doi: 10.1109/34.993558 – ident: ref71 doi: 10.1109/TPAMI.2010.57 – ident: ref36 doi: 10.1109/TPAMI.2002.1033219 – ident: ref10 doi: 10.1007/s00521-018-3836-z – ident: ref65 doi: 10.1016/j.patcog.2016.11.010 – ident: ref9 doi: 10.1016/j.patrec.2017.09.036 – ident: ref17 doi: 10.1214/aoms/1177700079 – ident: ref73 doi: 10.1109/TPAMI.2019.2907086 – ident: ref50 doi: 10.1016/j.patcog.2018.08.003 – start-page: 50 volume-title: Proc. Int. Conf. Neural Inf. Process. Syst. ident: ref43 article-title: Efficient pattern recognition using a new transformation distance – ident: ref76 doi: 10.1016/j.cag.2016.03.006 – ident: ref1 doi: 10.2307/1403797 – ident: ref41 doi: 10.1016/j.jss.2012.05.073 – start-page: 744 volume-title: Proc. 30th Int. Conf. Very Large Data Bases ident: ref24 article-title: Reverse kNN search in arbitrary dimensionality – ident: ref48 doi: 10.1109/TPAMI.2007.1140 – ident: ref77 doi: 10.1016/j.is.2016.03.003 – ident: ref45 doi: 10.1023/B:VISI.0000046589.39864.ee – ident: ref42 doi: 10.1109/TPAMI.2006.126 – ident: ref57 doi: 10.3390/info9090234 – ident: ref28 doi: 10.1016/j.neucom.2015.12.035 – ident: ref80 doi: 10.1109/TKDE.2019.2909204 – ident: ref33 doi: 10.1109/TSMCB.2007.908363 – ident: ref46 doi: 10.1109/CVPR.2005.320 – ident: ref55 doi: 10.1016/j.patrec.2018.03.023 – ident: ref52 doi: 10.1109/TPAMI.2009.55 – ident: ref23 doi: 10.7551/mitpress/1120.003.0090 – ident: ref3 doi: 10.1007/BF00994658 – ident: ref34 doi: 10.1155/2010/597373 – ident: ref6 doi: 10.1007/s10115-007-0114-2 – ident: ref51 doi: 10.1016/j.eswa.2018.08.021 – ident: ref67 doi: 10.1111/j.1469-1809.1936.tb02137.x – ident: ref11 doi: 10.1007/978-3-540-79299-4_7 – ident: ref75 doi: 10.1016/j.patcog.2019.106970 – ident: ref2 doi: 10.1109/TIT.1967.1053964 – ident: ref12 doi: 10.1145/2990508 – ident: ref21 doi: 10.1109/CVPR.2001.990456 – ident: ref39 doi: 10.1145/7902.7906 – volume: 9 start-page: 32 issue: 1 year: 2008 ident: ref16 article-title: Parimputation: From imputation and null-imputation to partially imputation publication-title: IEEE Intell. Informat. Bull. – ident: ref38 doi: 10.1007/BF00114779 – volume: 11 start-page: 24 issue: 1 year: 2010 ident: ref81 article-title: KNN-CF approach: Incorporating certainty factor to kNN classification publication-title: IEEE Intell. Informat. Bull. – ident: ref22 doi: 10.1198/016214501750332839 – ident: ref35 doi: 10.1109/TNNLS.2017.2673241 – ident: ref70 doi: 10.1109/TPAMI.2007.1182 – ident: ref32 doi: 10.1007/978-3-540-39964-3_62 – ident: ref47 doi: 10.1109/TPAMI.2004.1273978 – ident: ref58 doi: 10.1088/1742-6596/978/1/012047 – ident: ref63 doi: 10.1109/TPAMI.2013.83 – ident: ref27 doi: 10.1007/978-3-642-39787-5_10 – ident: ref49 doi: 10.1016/j.patcog.2005.08.016 – volume: 17 start-page: 513 volume-title: Proc. 17th Int. Conf. Neural Inf. Process. Syst. ident: ref62 article-title: Neighbourhood components analysis – volume: 18 start-page: 1473 volume-title: Proc. 18th Int. Conf. Neural Inf. Process. Syst. ident: ref61 article-title: Distance metric learning for large margin nearest neighbor classification – ident: ref29 doi: 10.1016/j.patcog.2017.09.038 – ident: ref13 doi: 10.1007/978-3-540-74976-9_25 – ident: ref69 doi: 10.1007/s10489-009-0207-6 – ident: ref26 doi: 10.1016/j.patcog.2006.12.019 – ident: ref60 doi: 10.1016/j.patcog.2018.04.024 – ident: ref4 doi: 10.1007/978-3-642-10844-0_50 – start-page: 985 volume-title: Proc. Int. Conf. Neural Inf. Process. Syst. ident: ref37 article-title: K-local hyperplane and convex distance nearest neighbor algorithms – ident: ref82 doi: 10.1016/j.neucom.2018.11.101 – ident: ref54 doi: 10.1016/S0167-8655(97)00112-8 – ident: ref59 doi: 10.1109/ACCESS.2019.2909752 – volume: 18 start-page: 451 volume-title: Proc. 18th Int. Conf. Neural Inf. Process. Syst. ident: ref66 article-title: Metric learning by collapsing classes – start-page: 2 volume-title: Proc. Workshop Adv. Concepts Intell. Vis. Syst. ident: ref20 article-title: Nearest neighbour strategies for image understanding – ident: ref31 doi: 10.1145/3319532 – ident: ref15 doi: 10.1007/s11704-018-7049-5 – ident: ref64 doi: 10.1145/1273496.1273523 – ident: ref68 doi: 10.1109/CVPR.2007.383124 – ident: ref8 doi: 10.1109/TKDE.2018.2858782 – ident: ref53 doi: 10.1016/0169-7439(87)80084-9 – ident: ref40 doi: 10.1007/3-540-48508-2_2 – ident: ref78 doi: 10.1109/TKDE.2016.2535287 – ident: ref74 doi: 10.14778/3397230.3397240 – start-page: 317 volume-title: Proc. 26th Annu. Conf. Learn. Theory ident: ref79 article-title: Randomized partition trees for exact nearest neighbor search – ident: ref7 doi: 10.1109/TKDE.2018.2873378
SSID	ssj0008781
Score	2.7117245
Snippet	The KNN algorithm is one of the most popular data mining algorithms. It has been widely and successfully applied to data analysis applications across a variety...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	4663
SubjectTerms	Algorithms Classification classification rule Data analysis Data mining KNN classification lazy learning Licenses Nearest neighbor methods Partitioning algorithms Prediction algorithms Training Training data
Title	Challenges in KNN Classification
URI	https://ieeexplore.ieee.org/document/9314060 https://www.proquest.com/docview/2714898332
Volume	34
WOSCitedRecordID	wos000853844700008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE/IET Electronic Library (IEL) (UW System Shared) customDbUrl: eissn: 1558-2191 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0008781 issn: 1041-4347 databaseCode: RIE dateStart: 19890101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PS8MwFH7M4UEPTjfF6ZQePInd0iRLmqPMDWFQPEzYraRpAgPZZD_8-02ytAwUwVsPSSlfmrx-fe97H8BDiZwe00mURTG0BCUpYqmQjqkWQ4NQgblB3myCZ1k6n4u3BjzVWhittS8-03136XP55Urt3K-ygSCWDjBL0I84Z3utVn3qptwbklp2YTkRoTxkMBMkBrPpy9gyQZz0XU4JO4n9QQzypio_TmIfXiat_z3YOZyFz8joeb_uF9DQyza0KouGKOzYNpwe9BvsQDSqrFM20WIZTbMs8qaYrlzIr9AlvE_Gs9FrHCwSYkUI28ZUcqkwl7xk0qSqlBoTQo2NR0qQkqjCBnBuw09aGq41lVoUUjCdKM5MwgwiV9Bcrpb6GiJNFWVqiBVxPeASJ2BVRSoUtvcxlOMuoAq0XIX-4c7G4iP3PAKJ3OGcO5zzgHMXHuspn_vmGX8N7jhg64EB0y70qpXJw_ba5JhbFidSQvDN77Nu4QQ7nYKvuutBc7ve6Ts4Vl_bxWZ979-cb997vS4
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PS8MwFH6MKagHp5vidGoPnsRuaZI2zVF0Y7JZPEzYLaRpCgPpZD_8-02yrgwUwVsPSSlfmrx-fe97H8Bdhqwe00qUeRoaghKkvlRI-1TzMEcoxSxHzmyCJUk8nfK3GjxUWhittSs-01176XL52Vyt7a-yHieGDkSGoO-FlGK0UWtV527MnCWp4ReGFRHKyhxmgHhvMnruGy6Ig67NKmErst-JQs5W5cdZ7ALMoPG_RzuB4_JD0nvcrPwp1HTRhMbWpMEr92wTjnY6DrbAe9qapyy9WeGNksRztpi2YMit0Rm8D_qTp6FfmiT4ipBo5VPJpMJMsiySeawyqTEhNDcRSXGSEZWaEM5MAIqznGlNpeap5JEOFIvyIMoROYd6MS_0BXiaKhqpECtiu8AFVsKq0pgrbO6TU4bbgLagCVV2ELdGFh_CMQnEhcVZWJxFiXMb7qspn5v2GX8Nbllgq4Elpm3obFdGlBtsKTAzPI7HhODL32fdwsFw8joW45dkdAWH2KoWXA1eB-qrxVpfw776Ws2Wixv3Fn0D5uHAdQ
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Challenges+in+KNN+Classification&rft.jtitle=IEEE+transactions+on+knowledge+and+data+engineering&rft.au=Zhang%2C+Shichao&rft.date=2022-10-01&rft.pub=IEEE&rft.issn=1041-4347&rft.volume=34&rft.issue=10&rft.spage=4663&rft.epage=4675&rft_id=info:doi/10.1109%2FTKDE.2021.3049250&rft.externalDocID=9314060
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1041-4347&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1041-4347&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1041-4347&client=summon