Grouped SMOTE With Noise Filtering Mechanism for Classifying Imbalanced Data

SMOTE (Synthetic Minority Oversampling TEchnique) is one of the most popular and well-known sampling algorithms for addressing class imbalance learning problem. The merits of SMOTE reflect at that in comparison with the random oversampling technique, it can alleviate the problem of overfitting to a...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE access Ročník 7; s. 170668 - 170681
Hlavní autori:	Cheng, Ke, Zhang, Chen, Yu, Hualong, Yang, Xibei, Zou, Haitao, Gao, Shang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms class imbalance learning Classification Classification algorithms Data models Estimation Filtering Filtering algorithms Filtration Gaussian-Mixture model Machine learning Noise measurement Outliers (statistics) Oversampling Parameters Probabilistic models probability density Safety Sampling SMOTE
ISSN:	2169-3536, 2169-3536
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	SMOTE (Synthetic Minority Oversampling TEchnique) is one of the most popular and well-known sampling algorithms for addressing class imbalance learning problem. The merits of SMOTE reflect at that in comparison with the random oversampling technique, it can alleviate the problem of overfitting to a large extent. However, two drawbacks of SMOTE have also been observed as follows, 1) it tends to propagate the noisy information in the procedure of oversampling; 2) it always assigns a global neighborhood parameter K but neglects the local distribution characteristics. To synchronously deal with these two problems, a grouped SMOTE algorithm with noise filtering mechanism (GSMOTE-NFM) is presented in this article. The algorithm firstly adopts Gaussian-Mixture Model (GMM) to explore the real distributions of the majority and minority classes, respectively. Then, most noisy instances can be removed by comparing the probability densities of the same instance in two different classes. Next, two new GMMs are constructed on the rest majority and minority class instances, respectively. Furthermore, all minority class instances can be divided into three different groups: safety, boundary and outlier, based on the corresponding probability density information. Finally, we assign an individual parameter K to the instances belonging to each specific group to generate new instances. We tested GSMOTE-NFM algorithm on 24 benchmark binary-class data sets with three popular classification models, and compared it with several state-of-the-art oversampling algorithms. The results indicate that our algorithm is significantly superior than the original SMOTE algorithm and several SMOTE-based modified methods.
AbstractList	SMOTE (Synthetic Minority Oversampling TEchnique) is one of the most popular and well-known sampling algorithms for addressing class imbalance learning problem. The merits of SMOTE reflect at that in comparison with the random oversampling technique, it can alleviate the problem of overfitting to a large extent. However, two drawbacks of SMOTE have also been observed as follows, 1) it tends to propagate the noisy information in the procedure of oversampling; 2) it always assigns a global neighborhood parameter K but neglects the local distribution characteristics. To synchronously deal with these two problems, a grouped SMOTE algorithm with noise filtering mechanism (GSMOTE-NFM) is presented in this article. The algorithm firstly adopts Gaussian-Mixture Model (GMM) to explore the real distributions of the majority and minority classes, respectively. Then, most noisy instances can be removed by comparing the probability densities of the same instance in two different classes. Next, two new GMMs are constructed on the rest majority and minority class instances, respectively. Furthermore, all minority class instances can be divided into three different groups: safety, boundary and outlier, based on the corresponding probability density information. Finally, we assign an individual parameter K to the instances belonging to each specific group to generate new instances. We tested GSMOTE-NFM algorithm on 24 benchmark binary-class data sets with three popular classification models, and compared it with several state-of-the-art oversampling algorithms. The results indicate that our algorithm is significantly superior than the original SMOTE algorithm and several SMOTE-based modified methods.
Author	Zou, Haitao Zhang, Chen Gao, Shang Cheng, Ke Yu, Hualong Yang, Xibei
Author_xml	– sequence: 1 givenname: Ke orcidid: 0000-0001-8034-9496 surname: Cheng fullname: Cheng, Ke organization: School of Computer, Jiangsu University of Science and Technology, Zhenjiang, China – sequence: 2 givenname: Chen orcidid: 0000-0002-8558-6543 surname: Zhang fullname: Zhang, Chen organization: School of Computer, Jiangsu University of Science and Technology, Zhenjiang, China – sequence: 3 givenname: Hualong orcidid: 0000-0001-9621-4158 surname: Yu fullname: Yu, Hualong organization: School of Computer, Jiangsu University of Science and Technology, Zhenjiang, China – sequence: 4 givenname: Xibei orcidid: 0000-0002-2182-0070 surname: Yang fullname: Yang, Xibei organization: School of Computer, Jiangsu University of Science and Technology, Zhenjiang, China – sequence: 5 givenname: Haitao orcidid: 0000-0001-8925-7125 surname: Zou fullname: Zou, Haitao organization: School of Computer, Jiangsu University of Science and Technology, Zhenjiang, China – sequence: 6 givenname: Shang orcidid: 0000-0002-1687-412X surname: Gao fullname: Gao, Shang email: gao_shang1972@163.com organization: School of Computer, Jiangsu University of Science and Technology, Zhenjiang, China
BookMark	eNp9kU9P4zAQxS3ESgssn4BLJM4t_hc3PqJQoFKBQ1nt0Ro7NrhK42K7h357XMKuVhzwxdb4_d7M6J2i4yEMFqELgqeEYHl13bbz1WpKMZFTKusaN-IInVAi5ITVTBz_9_6JzlNa43KaUqpnJ2h5F8Nua7tq9fD0PK_--PxaPQafbHXr-2yjH16qB2teYfBpU7kQq7aHlLzbH34WGw09DKbwN5DhF_rhoE_2_PM-Q79v58_t_WT5dLdor5cTw3GTJ044prUBI4yuO8lYV8-ACg7UGOuMMQ3lhpVVRMct8KZzjnUUS9CO0SJkZ2gx-nYB1mob_QbiXgXw6qMQ4ouCmL3prXJAobTQQlPKHbbaEawb1gkMDXACxety9NrG8LazKat12MWhjK8or2vBBJe4qOSoMjGkFK1TxmfIPgw5gu8VweqQhRqzUIcs1GcWhWVf2L8Tf09djJS31v4jGkmwIDP2DjEnl2c
CODEN	IAECCG
CitedBy_id	crossref_primary_10_2147_RMHP_S331077 crossref_primary_10_1016_j_knosys_2022_110235 crossref_primary_10_1109_TAI_2022_3160658 crossref_primary_10_1186_s40537_025_01119_4 crossref_primary_10_1016_j_ins_2023_119621 crossref_primary_10_1016_j_jksuci_2022_06_005 crossref_primary_10_1016_j_compeleceng_2021_107370 crossref_primary_10_1186_s13036_022_00319_3 crossref_primary_10_1007_s10796_024_10533_7 crossref_primary_10_1016_j_ins_2022_02_038 crossref_primary_10_3389_fonc_2021_601425 crossref_primary_10_1016_j_asoc_2023_110895 crossref_primary_10_1007_s40009_023_01249_4 crossref_primary_10_1057_s41599_023_02382_7 crossref_primary_10_7717_peerj_cs_2682 crossref_primary_10_1186_s40635_025_00724_0 crossref_primary_10_1038_s41598_025_09506_w crossref_primary_10_1061__ASCE_CO_1943_7862_0002124 crossref_primary_10_1016_j_engstruct_2025_120754 crossref_primary_10_1016_j_ins_2020_10_013 crossref_primary_10_1007_s11042_024_19528_7 crossref_primary_10_1088_1361_6501_acf0df crossref_primary_10_2478_fcds_2025_0009 crossref_primary_10_1016_j_isprsjprs_2023_05_015 crossref_primary_10_1109_ACCESS_2022_3185129 crossref_primary_10_32604_cmc_2023_046187 crossref_primary_10_1109_TKDE_2022_3179381 crossref_primary_10_1016_j_aei_2024_102606 crossref_primary_10_1017_aer_2025_10053 crossref_primary_10_3390_info14010054 crossref_primary_10_1016_j_eswa_2022_117023 crossref_primary_10_1049_itr2_12267 crossref_primary_10_1109_ACCESS_2020_3047838 crossref_primary_10_3390_math13030368 crossref_primary_10_3390_mi15121501 crossref_primary_10_2166_wpt_2025_109 crossref_primary_10_1016_j_eswa_2021_116213 crossref_primary_10_1007_s10489_022_03408_4 crossref_primary_10_1016_j_eswa_2023_120379 crossref_primary_10_1007_s10845_021_01873_1 crossref_primary_10_1016_j_knosys_2024_112236 crossref_primary_10_1016_j_knosys_2025_113580 crossref_primary_10_1109_JIOT_2025_3526160 crossref_primary_10_1109_ACCESS_2023_3303509 crossref_primary_10_1109_ACCESS_2020_2975630 crossref_primary_10_1007_s13755_020_00112_w
Cites_doi	10.1109/TR.2013.2259203 10.1007/978-3-642-01307-2_43 10.1016/j.jtbi.2014.10.008 10.1109/TCSS.2014.2377811 10.3390/app8091597 10.1016/j.neunet.2015.06.005 10.1016/j.neucom.2012.08.018 10.1145/1273496.1273614 10.1109/IJCNN.2010.5596787 10.1016/j.knosys.2015.10.012 10.1145/2601248.2601294 10.2991/ijcis.10.1.82 10.1109/TSMCA.2009.2029559 10.1109/TKDE.2006.17 10.1109/TCYB.2016.2579658 10.1177/1536867X1601600407 10.1016/j.sbspro.2012.09.168 10.1016/j.knosys.2014.12.007 10.1007/s11280-012-0178-0 10.1109/ACCESS.2018.2789428 10.1016/j.patcog.2007.04.009 10.1016/j.neucom.2015.10.042 10.1109/TKDE.2014.2345380 10.1007/s12559-014-9301-0 10.1016/j.ins.2014.08.051 10.1109/ACCESS.2019.2931865 10.1007/s13748-012-0027-5 10.1016/j.ins.2009.12.010 10.1145/1007730.1007733 10.1109/LSENS.2018.2879990 10.1016/j.knosys.2011.01.012 10.1145/3343440 10.1142/S0219622006002258 10.1109/JBHI.2014.2332001 10.1109/TSMCC.2011.2161285 10.1109/ACCESS.2018.2839340 10.5705/ss.2014.105 10.1016/j.eswa.2016.12.035 10.1613/jair.953 10.1109/TNNLS.2013.2246188 10.1371/journal.pone.0139654
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019
DBID	97E ESBDL RIA RIE AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D DOA
DOI	10.1109/ACCESS.2019.2955086
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present Open Access资源_IEL Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts METADEX Technology Research Database Materials Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Materials Research Database Engineered Materials Abstracts 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 METADEX Computer and Information Systems Abstracts Professional
DatabaseTitleList	Materials Research Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	2169-3536
EndPage	170681
ExternalDocumentID	oai_doaj_org_article_fa2ad93b6b224f0ebf10b83d60a8a41a 10_1109_ACCESS_2019_2955086 8910617
Genre	orig-research
GrantInformation_xml	– fundername: Jiangsu Planned Projects for Postdoctoral Research Funds grantid: 1401037B funderid: 10.13039/501100010242 – fundername: Open Project of Artificial Intelligence Key Laboratory of Sichuan Province grantid: 2019RYJ02 – fundername: Natural Science Foundation of Jiangsu Province grantid: BK20191457 funderid: 10.13039/501100004608 – fundername: National Natural Science Foundation of China grantid: 61305058; 61572242 funderid: 10.13039/501100001809 – fundername: China Postdoctoral Science Foundation grantid: 2013M540404; 2015T80481 funderid: 10.13039/501100002858 – fundername: Qing Lan Project of Jiangsu Province of China
GroupedDBID	0R~ 4.4 5VS 6IK 97E AAJGR ABAZT ABVLG ACGFS ADBBV AGSQL ALMA_UNASSIGNED_HOLDINGS BCNDV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD ESBDL GROUPED_DOAJ IPLJI JAVBF KQ8 M43 M~E O9- OCL OK1 RIA RIE RNS AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D RIG
ID	FETCH-LOGICAL-c408t-f6f3bbcac6cb5d933d57a264a2ccefccc824c32956d4ea48dff3d209abf32a263
IEDL.DBID	RIE
ISICitedReferencesCount	51
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000560454900117&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2169-3536
IngestDate	Fri Oct 03 12:29:46 EDT 2025 Mon Jun 30 03:47:42 EDT 2025 Sat Nov 29 02:41:36 EST 2025 Tue Nov 18 21:49:03 EST 2025 Wed Aug 27 02:40:21 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Language	English
License	https://creativecommons.org/licenses/by/4.0/legalcode
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c408t-f6f3bbcac6cb5d933d57a264a2ccefccc824c32956d4ea48dff3d209abf32a263
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-8558-6543 0000-0002-1687-412X 0000-0001-9621-4158 0000-0002-2182-0070 0000-0001-8034-9496 0000-0001-8925-7125
OpenAccessLink	https://ieeexplore.ieee.org/document/8910617
PQID	2455636490
PQPubID	4845423
PageCount	14
ParticipantIDs	proquest_journals_2455636490 crossref_citationtrail_10_1109_ACCESS_2019_2955086 ieee_primary_8910617 doaj_primary_oai_doaj_org_article_fa2ad93b6b224f0ebf10b83d60a8a41a crossref_primary_10_1109_ACCESS_2019_2955086
PublicationCentury	2000
PublicationDate	20190000 2019-00-00 20190101 2019-01-01
PublicationDateYYYYMMDD	2019-01-01
PublicationDate_xml	– year: 2019 text: 20190000
PublicationDecade	2010
PublicationPlace	Piscataway
PublicationPlace_xml	– name: Piscataway
PublicationTitle	IEEE access
PublicationTitleAbbrev	Access
PublicationYear	2019
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 ref12 ref15 japkowicz (ref3) 2000 ref14 ref10 ref17 ref16 ref19 ref18 ref50 ref46 ref45 ref47 ref42 ref41 ref44 ref49 ref8 ref7 he (ref36) 2005 ref9 ref40 ref35 ref34 ref37 ref31 ref30 ref33 chawla (ref5) 2009 ref32 ref2 ref1 engen (ref11) 2008; 12 bunkhumpornpat (ref39) 2009 ref24 han (ref38) 2005 ref23 ref26 ref25 ref20 ref22 ref21 chawla (ref4) 2003 ref28 ref27 ref29 demšar (ref48) 2006; 7 blake (ref43) 1998 wang (ref6) 2017
References_xml	– start-page: 1 year: 2003 ident: ref4 article-title: Workshop learning from imbalanced data sets II publication-title: Proc Int Conf Mach Learn – ident: ref15 doi: 10.1109/TR.2013.2259203 – start-page: 475 year: 2009 ident: ref39 article-title: Safe-level-SMOTE: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem publication-title: Proc Pacific-Asia Conf Knowl Discovery Data Mining doi: 10.1007/978-3-642-01307-2_43 – ident: ref18 doi: 10.1016/j.jtbi.2014.10.008 – ident: ref21 doi: 10.1109/TCSS.2014.2377811 – ident: ref14 doi: 10.3390/app8091597 – ident: ref27 doi: 10.1016/j.neunet.2015.06.005 – ident: ref24 doi: 10.1016/j.neucom.2012.08.018 – ident: ref37 doi: 10.1145/1273496.1273614 – ident: ref22 doi: 10.1109/IJCNN.2010.5596787 – ident: ref31 doi: 10.1016/j.knosys.2015.10.012 – ident: ref16 doi: 10.1145/2601248.2601294 – ident: ref44 doi: 10.2991/ijcis.10.1.82 – ident: ref34 doi: 10.1109/TSMCA.2009.2029559 – ident: ref29 doi: 10.1109/TKDE.2006.17 – year: 1998 ident: ref43 article-title: UCI repository of machine learning databases – ident: ref35 doi: 10.1109/TCYB.2016.2579658 – ident: ref45 doi: 10.1177/1536867X1601600407 – ident: ref17 doi: 10.1016/j.sbspro.2012.09.168 – volume: 7 start-page: 1 year: 2006 ident: ref48 article-title: Statistical comparisons of classifiers over multiple data sets publication-title: J Mach Learn Res – ident: ref30 doi: 10.1016/j.knosys.2014.12.007 – ident: ref9 doi: 10.1007/s11280-012-0178-0 – start-page: 537 year: 2005 ident: ref36 article-title: An over-sampling expert system for learing from imbalanced data sets publication-title: Proc Int Conf Neural Netw Brain – ident: ref13 doi: 10.1109/ACCESS.2018.2789428 – volume: 12 start-page: 357 year: 2008 ident: ref11 article-title: Enhancing network based intrusion detection for imbalanced data publication-title: Int J Knowl -Based Intell Eng Syst – ident: ref25 doi: 10.1016/j.patcog.2007.04.009 – ident: ref10 doi: 10.1016/j.neucom.2015.10.042 – ident: ref33 doi: 10.1109/TKDE.2014.2345380 – ident: ref20 doi: 10.1007/s12559-014-9301-0 – ident: ref40 doi: 10.1016/j.ins.2014.08.051 – ident: ref47 doi: 10.1109/ACCESS.2019.2931865 – start-page: 10 year: 2000 ident: ref3 article-title: Learning from imbalanced data sets: A comparison of various strategies publication-title: Proc Amer Assoc Artif Intell (AAAI) Workshop – ident: ref41 doi: 10.1007/s13748-012-0027-5 – ident: ref49 doi: 10.1016/j.ins.2009.12.010 – ident: ref7 doi: 10.1145/1007730.1007733 – ident: ref12 doi: 10.1109/LSENS.2018.2879990 – ident: ref50 doi: 10.1016/j.knosys.2011.01.012 – start-page: 1 year: 2017 ident: ref6 publication-title: Proc IJCAI Workshop Learn Presence Class Imbalance Concept Drift (LPCICD) – start-page: 878 year: 2005 ident: ref38 article-title: Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning publication-title: Proc Int Conf Intell Comput – ident: ref1 doi: 10.1145/3343440 – ident: ref8 doi: 10.1142/S0219622006002258 – ident: ref46 doi: 10.1109/JBHI.2014.2332001 – ident: ref32 doi: 10.1109/TSMCC.2011.2161285 – ident: ref28 doi: 10.1109/ACCESS.2018.2839340 – ident: ref42 doi: 10.5705/ss.2014.105 – ident: ref2 doi: 10.1016/j.eswa.2016.12.035 – ident: ref23 doi: 10.1613/jair.953 – start-page: 1 year: 2009 ident: ref5 article-title: Workshop on data mining when classes are imbalanced and errors have costs publication-title: Proc 13th Pacific-Asia Conf Adv Knowl Discovery Data Mining – ident: ref26 doi: 10.1109/TNNLS.2013.2246188 – ident: ref19 doi: 10.1371/journal.pone.0139654
SSID	ssj0000816957
Score	2.4173362
Snippet	SMOTE (Synthetic Minority Oversampling TEchnique) is one of the most popular and well-known sampling algorithms for addressing class imbalance learning...
SourceID	doaj proquest crossref ieee
SourceType	Open Website Aggregation Database Enrichment Source Index Database Publisher
StartPage	170668
SubjectTerms	Algorithms class imbalance learning Classification Classification algorithms Data models Estimation Filtering Filtering algorithms Filtration Gaussian-Mixture model Machine learning Noise measurement Outliers (statistics) Oversampling Parameters Probabilistic models probability density Safety Sampling SMOTE
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LSwMxEA5SPOhB1CrWFzl4dDWbTbObY60tCloF6-MW8sQFrWJXf7-T7LYUBL14XWYf-TLJfMlmvkHoyHch8GitEqssS1hqYcylxCZAbVMnqHckZsg9XOWjUfH0JG4XSn2FM2G1PHAN3KlXVFmRaa4h2HjitE-JLjLLiSoUSyM1IrlYWEzFObhIuejmjcxQSsRpr9-HFoWzXOKECuDlIXt6IRRFxf6mxMqPeTkGm-E6WmtYIu7VX7eBltxkE60uaAe20VXcNXIW313fjAf4saye8eitnDo8LMMfcDDC1y7k9ZbTVwzUFMf6l2XMa8KXrzqcaTRw_7mq1Ba6Hw7G_YukKY2QGEaKKvHcZ1obZbjRXYAns91cAbdR1BjnjTEFZSaDdnLLnGKF9T6zlAilfUbBMNtGrcnbxO0grIGjGOG45kYxYYAxeE0AaBju3ipCOojOUJKm0Q0P5SteZFw_ECFraGWAVjbQdtDx_Kb3Wjbjd_OzAP_cNGhexwvgCbLxBPmXJ3RQO3Te_CGFCMvdvIP2Z50pm_E5lZQFYTTOBNn9j1fvoZXQnHprZh-1qo9Pd4CWzVdVTj8Oo2t-AyI15oI priority: 102 providerName: Directory of Open Access Journals
Title	Grouped SMOTE With Noise Filtering Mechanism for Classifying Imbalanced Data
URI	https://ieeexplore.ieee.org/document/8910617 https://www.proquest.com/docview/2455636490 https://doaj.org/article/fa2ad93b6b224f0ebf10b83d60a8a41a
Volume	7
WOSCitedRecordID	wos000560454900117&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NT9wwEB0B6qE99ItWXQrIhx4JOI7XiY90u6tWYreVCpSb5U8RqexWbODIb2fsmAiJqlIvURSNI9svjp_HnjcAn8IYJx5jdOG04wUvHY65kroCqW3pJQuepgi585N6sWguLuSPDTgYYmG89-nwmT-Mt2kv363sTXSVHTUyLmDqTdisa9HHag3-lJhAQo7rLCxUUnl0PJlgG-LpLXnIJDLxGC_9aPJJGv05qcqTP3GaXmav_q9ir-FlppHkuMf9DWz45Vt48UhccBtOklvJO_Jz_v10Sn613SVZrNq1J7M2bpGjEZn7GPjbrq8IcleSEmS2KfCJfLsy8dCjxfJfdKffwdlsejr5WuTcCYXltOmKIEJljNVWWDN2sqrcuNZIfjSz1gdrbcO4rbBbhONe88aFUDlGpTahYmhYvYet5WrpPwAxSGKs9MIIq7m0SCmCoYF6_B8EpykdAXvoVGWzsHjMb_FbpQUGlapHQkUkVEZiBAdDoT-9rsa_zT9HtAbTKIqdHiAMKo8xFTTT2FQjDPISrKAJJTVN5QTVjealHsF2hG54SUZtBLsP2Ks8gNeK8aicJrikO38v9RGexwr23phd2Oqub_wePLO3Xbu-3k9Le7zO76b76Tu9Bz705Lo
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PTxQxFH4BJEEPKKJxBbUHjwx0Op3Z6REXNhB3VxMW5Nb0Z5xEdg07-Pf72ikTEoiJt8nkddL2m7ZfX_u-B_DZl7jwaK0yqyzPeG5xzOXUZkhtcyeYdzRGyF1NhrNZfX0tvq_BQR8L45yLl8_cYXiMZ_l2ae6Cq-yoFmEDM1yHZyXHfU8XrdV7VEIKCVEOk7RQTsXR8WiErQj3t8QhE8jFQ8T0g-UnqvSntCqP5uK4wIxf_l_VXsF2IpLkuEN-B9bc4jW8eCAvuAuT6FhyllxMv81PyY-m_Ulmy2blyLgJh-RoRKYuhP42qxuC7JXEFJlNDH0i5zc6XHs0WP5EteoNXI5P56OzLGVPyAyndZv5yhdaG2Uqo0srisKWQ4X0RzFjnDfG1IybArulstwpXlvvC8uoUNoXDA2Lt7CxWC7cOyAaaYwRrtKVUVwYJBVeU08dzgjeKkoHwO47VZokLR4yXPyScYtBheyQkAEJmZAYwEFf6HenrPFv8y8Brd40yGLHFwiDTKNMesUUNlVXGpkJVlD7nOq6sBVVteK5GsBugK7_SEJtAPv32Ms0hFeS8aCdVnFB3z9d6hNsnc2nEzk5n33dg-ehsp1vZh822ts79wE2zZ-2Wd1-jP_pX3_u5ds
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=Grouped+SMOTE+With+Noise+Filtering+Mechanism+for+Classifying+Imbalanced+Data&rft.jtitle=IEEE+access&rft.au=Cheng%2C+Ke&rft.au=Zhang%2C+Chen&rft.au=Yu%2C+Hualong&rft.au=Yang%2C+Xibei&rft.date=2019&rft.pub=IEEE&rft.eissn=2169-3536&rft.volume=7&rft.spage=170668&rft.epage=170681&rft_id=info:doi/10.1109%2FACCESS.2019.2955086&rft.externalDocID=8910617
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2169-3536&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2169-3536&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2169-3536&client=summon