{"id":"https://openalex.org/W4416234105","doi":"https://doi.org/10.3390/sym17111941","title":"CS-SMOTE: An Improved Oversampling Method Combining SMOTE Method and Symmetrical Cube Scoring Mechanism","display_name":"CS-SMOTE: An Improved Oversampling Method Combining SMOTE Method and Symmetrical Cube Scoring Mechanism","publication_year":2025,"publication_date":"2025-11-12","ids":{"openalex":"https://openalex.org/W4416234105","doi":"https://doi.org/10.3390/sym17111941"},"language":"en","primary_location":{"id":"doi:10.3390/sym17111941","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17111941","pdf_url":null,"source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/sym17111941","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019972361","display_name":"Shihao Song","orcid":"https://orcid.org/0009-0000-6327-6272"},"institutions":[{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shihao Song","raw_affiliation_strings":["School of Science, Dalian Maritime University, Dalian 116026, China"],"raw_orcid":"https://orcid.org/0009-0000-6327-6272","affiliations":[{"raw_affiliation_string":"School of Science, Dalian Maritime University, Dalian 116026, China","institution_ids":["https://openalex.org/I43313876"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024598357","display_name":"Sibo Yang","orcid":"https://orcid.org/0000-0002-2301-3803"},"institutions":[{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Sibo Yang","raw_affiliation_strings":["School of Science, Dalian Maritime University, Dalian 116026, China"],"raw_orcid":"https://orcid.org/0000-0002-2301-3803","affiliations":[{"raw_affiliation_string":"School of Science, Dalian Maritime University, Dalian 116026, China","institution_ids":["https://openalex.org/I43313876"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107861887","display_name":"M. Sun","orcid":"https://orcid.org/0009-0003-2197-826X"},"institutions":[{"id":"https://openalex.org/I107982497","display_name":"Dalian Ocean University","ror":"https://ror.org/0523b6g79","country_code":"CN","type":"education","lineage":["https://openalex.org/I107982497"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengqi Sun","raw_affiliation_strings":["College of Fisheries and Life Science, Dalian Ocean University, Dalian 116023, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Fisheries and Life Science, Dalian Ocean University, Dalian 116023, China","institution_ids":["https://openalex.org/I107982497"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5024598357"],"corresponding_institution_ids":["https://openalex.org/I43313876"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16951294,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":"11","first_page":"1941","last_page":"1941"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.7213000059127808,"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":0.7213000059127808,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.07890000194311142,"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/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.027899999171495438,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.9625999927520752},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.47749999165534973},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4115000069141388},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.38420000672340393},{"id":"https://openalex.org/keywords/cube","display_name":"Cube (algebra)","score":0.37700000405311584},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.3650999963283539},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.3553999960422516},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.3497999906539917}],"concepts":[{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.9625999927520752},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7206000089645386},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5116999745368958},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.47749999165534973},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4603999853134155},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4302999973297119},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4115000069141388},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.38420000672340393},{"id":"https://openalex.org/C53051483","wikidata":"https://www.wikidata.org/wiki/Q861555","display_name":"Cube (algebra)","level":2,"score":0.37700000405311584},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.3650999963283539},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.3553999960422516},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.34940001368522644},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3172000050544739},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.31189998984336853},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.2904999852180481},{"id":"https://openalex.org/C173642442","wikidata":"https://www.wikidata.org/wiki/Q1253346","display_name":"Decimation","level":3,"score":0.28859999775886536},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.288100004196167},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3390/sym17111941","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17111941","pdf_url":null,"source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3390/sym17111941","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17111941","pdf_url":null,"source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"For":[0],"classification":[1],"problems,":[2],"an":[3,63],"imbalanced":[4],"dataset":[5],"can":[6],"seriously":[7],"reduce":[8],"the":[9,31,37,44,80,107,124,145,154,170,173,189,212,221,245],"learning":[10],"efficiency":[11],"in":[12,131,161,220,248,272,285],"machine":[13],"learning.":[14],"In":[15,169,199],"order":[16],"to":[17,85,106,122,126,144,150,179,201],"solve":[18],"this":[19],"problem,":[20],"many":[21],"scholars":[22],"have":[23],"proposed":[24,155,174],"a":[25,71,87,209,261],"series":[26],"of":[27,43,83,163,191,214,263,274],"methods":[28,193],"mainly":[29],"from":[30],"data":[32,38,125],"and":[33,111,135,166,186,231,257,268],"algorithm":[34,77],"levels.":[35],"At":[36],"level,":[39],"SMOTE":[40,152],"is":[41,120,218],"one":[42],"most":[45],"effective":[46],"methods;":[47],"it":[48],"creates":[49],"new":[50,88,113,137],"minority":[51,58],"samples":[52,114,138,181],"through":[53,115],"linearly":[54],"interpolating":[55],"between":[56],"existing":[57,151],"samples.":[59],"This":[60,76,119],"paper":[61],"proposes":[62],"improved":[64],"SMOTE-based":[65],"data-level":[66],"oversampling":[67,184,216,241],"method":[68,156,175,217],"that":[69,132],"leverages":[70],"symmetrical":[72],"cube":[73],"scoring":[74,89],"mechanism.":[75],"first":[78,246,271],"exploits":[79],"symmetry":[81],"properties":[82],"cubes":[84],"construct":[86],"rule":[90],"based":[91],"on":[92,208,252],"different":[93,183],"symmetric":[94],"neighboring":[95],"cubes,":[96],"thereby":[97],"dynamically":[98],"selecting":[99,129],"sample":[100],"points.":[101],"It":[102],"then":[103,187],"maps":[104],"back":[105,143],"original":[108],"dimensional":[109],"space,":[110,134],"generates":[112],"multiple":[116],"linear":[117],"interpolations.":[118],"equivalent":[121],"reducing":[123],"three":[127,225],"dimensions,":[128],"points":[130,142],"three-dimensional":[133],"synthesizing":[136],"by":[139,194,206],"mapping":[140],"those":[141],"corresponding":[146],"high-dimensional":[147],"space.":[148],"Compared":[149],"variants,":[153],"delivers":[157],"more":[158],"targeted":[159],"performance":[160,213,259],"regions":[162],"varying":[164],"densities":[165],"boundary":[167],"areas.":[168],"experimental":[171,222,234],"section,":[172],"selects":[176],"several":[177],"datasets":[178],"synthesize":[180],"under":[182],"methods,":[185,242],"compare":[188],"performances":[190],"these":[192],"calculating":[195],"some":[196],"evaluation":[197],"indicators.":[198],"addition,":[200],"avoid":[202],"accidental":[203],"results":[204,235],"caused":[205],"relying":[207],"single":[210],"classifier,":[211],"each":[215],"tested":[219],"section":[223],"using":[224],"commonly":[226],"used":[227],"classifiers":[228],"(SVM,":[229],"ELM,":[230],"MLP).":[232],"The":[233],"show":[236],"that,":[237],"compared":[238],"with":[239],"other":[240],"CS-SMOTE":[243,269],"achieves":[244],"place":[247],"average":[249],"ranking.":[250],"Based":[251],"33":[253],"datasets,":[254],"3":[255,258],"classifiers,":[256],"metrics,":[260],"total":[262],"297":[264],"rankings":[265],"were":[266],"obtained,":[267],"ranked":[270],"179":[273],"them,":[275],"accounting":[276],"for":[277],"60.27%,":[278],"which":[279],"clearly":[280],"demonstrates":[281],"its":[282],"strong":[283],"capability":[284],"addressing":[286],"class-imbalanced":[287],"problems.":[288]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-11-13T00:00:00"}