{"id":"https://openalex.org/W4405718128","doi":"https://doi.org/10.1109/access.2024.3521120","title":"MKC-SMOTE: A Novel Synthetic Oversampling Method for Multi-Class Imbalanced Data Classification","display_name":"MKC-SMOTE: A Novel Synthetic Oversampling Method for Multi-Class Imbalanced Data Classification","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4405718128","doi":"https://doi.org/10.1109/access.2024.3521120"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3521120","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3521120","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2024.3521120","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103341938","display_name":"Jiao Wang","orcid":"https://orcid.org/0000-0001-6022-1807"},"institutions":[{"id":"https://openalex.org/I139322472","display_name":"Universiti Sains Malaysia","ror":"https://ror.org/02rgb2k63","country_code":"MY","type":"education","lineage":["https://openalex.org/I139322472"]}],"countries":["MY"],"is_corresponding":true,"raw_author_name":"Jiao Wang","raw_affiliation_strings":["School of Mathematical Sciences, Universiti Sains Malaysia, Penang, Malaysia"],"raw_orcid":"https://orcid.org/0000-0001-6022-1807","affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Universiti Sains Malaysia, Penang, Malaysia","institution_ids":["https://openalex.org/I139322472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011238188","display_name":"Norhashidah Awang","orcid":"https://orcid.org/0000-0002-2280-7193"},"institutions":[{"id":"https://openalex.org/I139322472","display_name":"Universiti Sains Malaysia","ror":"https://ror.org/02rgb2k63","country_code":"MY","type":"education","lineage":["https://openalex.org/I139322472"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Norhashidah Awang","raw_affiliation_strings":["School of Mathematical Sciences, Universiti Sains Malaysia, Penang, Malaysia"],"raw_orcid":"https://orcid.org/0000-0002-2280-7193","affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Universiti Sains Malaysia, Penang, Malaysia","institution_ids":["https://openalex.org/I139322472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103341938"],"corresponding_institution_ids":["https://openalex.org/I139322472"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.3016,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.8460586,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"12","issue":null,"first_page":"196929","last_page":"196938"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9534000158309937,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9235000014305115,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.9370945692062378},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6947146058082581},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.6527110934257507},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5589675307273865},{"id":"https://openalex.org/keywords/data-classification","display_name":"Data classification","score":0.5022861957550049},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.478561669588089},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4522472321987152},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44933056831359863},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.42735961079597473},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.07273152470588684},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.057313889265060425}],"concepts":[{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.9370945692062378},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6947146058082581},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6527110934257507},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5589675307273865},{"id":"https://openalex.org/C2780724565","wikidata":"https://www.wikidata.org/wiki/Q5227256","display_name":"Data classification","level":2,"score":0.5022861957550049},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.478561669588089},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4522472321987152},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44933056831359863},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.42735961079597473},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.07273152470588684},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.057313889265060425}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3521120","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3521120","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:01b1b318ed4a48a1a894caef7b65ece4","is_oa":true,"landing_page_url":"https://doaj.org/article/01b1b318ed4a48a1a894caef7b65ece4","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 196929-196938 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3521120","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3521120","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1941659294","https://openalex.org/W2012035409","https://openalex.org/W2024223694","https://openalex.org/W2117190680","https://openalex.org/W2118978333","https://openalex.org/W2126996952","https://openalex.org/W2132791018","https://openalex.org/W2135248138","https://openalex.org/W2148143831","https://openalex.org/W2164341120","https://openalex.org/W2185967890","https://openalex.org/W2223903364","https://openalex.org/W2300921526","https://openalex.org/W2338318698","https://openalex.org/W2601726217","https://openalex.org/W2736435690","https://openalex.org/W2766296277","https://openalex.org/W2768149277","https://openalex.org/W2783151797","https://openalex.org/W2794124609","https://openalex.org/W2794290001","https://openalex.org/W2998823736","https://openalex.org/W3024905798","https://openalex.org/W3041664846","https://openalex.org/W3082059448","https://openalex.org/W3096363578","https://openalex.org/W3183862858","https://openalex.org/W3189035319","https://openalex.org/W4223946313","https://openalex.org/W4226049722","https://openalex.org/W4243367342","https://openalex.org/W4285174182","https://openalex.org/W4293162767","https://openalex.org/W4308210155","https://openalex.org/W4388817724","https://openalex.org/W4400269175","https://openalex.org/W6697106032","https://openalex.org/W6842296903","https://openalex.org/W6855607130"],"related_works":["https://openalex.org/W2953675148","https://openalex.org/W2300921526","https://openalex.org/W4386210978","https://openalex.org/W2772832743","https://openalex.org/W3213683101","https://openalex.org/W3035095237","https://openalex.org/W3186233728","https://openalex.org/W4200459988","https://openalex.org/W2734791975","https://openalex.org/W4237698281"],"abstract_inverted_index":{"The":[0,164],"learning":[1],"of":[2,32,82,140,176],"multi-class":[3,50,68,177],"imbalance":[4,17,27,41,51,157],"problems":[5],"presents":[6],"greater":[7],"challenges":[8],"and":[9,43,62,85,161,180,184],"has":[10],"fewer":[11],"research":[12],"results":[13,165],"compared":[14],"to":[15,24,49,92],"binary":[16,40],"problems.":[18,28],"Resampling":[19],"techniques":[20],"are":[21,36,119],"widely":[22],"employed":[23],"address":[25],"data":[26],"However,":[29],"the":[30,57,72,75,80,87,112,122,126,137,141,152,168,173],"majority":[31],"existing":[33],"resampling":[34],"methods":[35,160],"designed":[37],"specifically":[38,65],"for":[39,67,97,109],"datasets":[42,179],"demonstrate":[44,166],"significant":[45],"limitations":[46],"when":[47],"applied":[48],"datasets.":[52,70],"Therefore,":[53],"this":[54],"study":[55],"introduces":[56],"MKC-SMOTE":[58,127,153,169],"algorithm,":[59],"a":[60],"novel":[61],"effective":[63],"method":[64],"tailored":[66],"imbalanced":[69,178],"During":[71],"pre-processing":[73],"phase,":[74,114],"algorithm":[76,91,108,128,154,170],"takes":[77],"into":[78],"account":[79],"distribution":[81],"all":[83],"classes":[84],"employs":[86],"k-nearest":[88],"neighbors":[89],"(kNN)":[90],"identify":[93],"appropriate":[94],"original":[95],"samples":[96,118,133],"synthesizing":[98],"minority":[99,131],"class":[100,132],"samples.":[101],"It":[102],"then":[103],"utilizes":[104],"an":[105],"enhanced":[106],"SMOTE":[107],"interpolation.":[110],"In":[111],"post-processing":[113],"potentially":[115],"misleading":[116],"synthesized":[117],"eliminated":[120],"by":[121,134],"undersampling":[123],"technique.":[124],"Consequently,":[125],"generates":[129],"high-quality":[130],"strategically":[135],"exploring":[136],"distributional":[138],"regions":[139],"classes.":[142],"Extensive":[143],"experiments":[144],"were":[145],"conducted":[146],"on":[147],"21":[148],"real-world":[149],"datasets,":[150],"comparing":[151],"with":[155],"six":[156],"problem":[158],"handling":[159],"two":[162],"classifiers.":[163],"that":[167],"significantly":[171],"enhances":[172],"classification":[174],"performance":[175],"outperforms":[181],"several":[182],"popular":[183],"state-of-the-art":[185],"oversampling":[186],"methods.":[187]},"counts_by_year":[{"year":2025,"cited_by_count":4}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
