{"id":"https://openalex.org/W7125955271","doi":"https://doi.org/10.1007/s44443-026-00494-z","title":"An improved SMOTE method based on triangular scoring mechanism with random perturbation for handling class-imbalanced classification problems","display_name":"An improved SMOTE method based on triangular scoring mechanism with random perturbation for handling class-imbalanced classification problems","publication_year":2026,"publication_date":"2026-01-28","ids":{"openalex":"https://openalex.org/W7125955271","doi":"https://doi.org/10.1007/s44443-026-00494-z"},"language":"en","primary_location":{"id":"doi:10.1007/s44443-026-00494-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44443-026-00494-z","pdf_url":null,"source":{"id":"https://openalex.org/S2764955546","display_name":"Journal of King Saud University - Computer and Information Sciences","issn_l":"1319-1578","issn":["1319-1578","2213-1248"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of King Saud University Computer and Information Sciences","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1007/s44443-026-00494-z","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019972361","display_name":"Shihao Song","orcid":"https://orcid.org/0009-0002-3606-7277"},"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, No. 1 Linghai Road, Ganjingzi District, 116026, Dalian, Liaoning Province, China"],"affiliations":[{"raw_affiliation_string":"School of Science, Dalian Maritime University, No. 1 Linghai Road, Ganjingzi District, 116026, Dalian, Liaoning Province, China","institution_ids":["https://openalex.org/I43313876"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5104433023","display_name":"Sibo Yang","orcid":null},"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, No. 1 Linghai Road, Ganjingzi District, 116026, Dalian, Liaoning Province, China"],"affiliations":[{"raw_affiliation_string":"School of Science, Dalian Maritime University, No. 1 Linghai Road, Ganjingzi District, 116026, Dalian, Liaoning Province, China","institution_ids":["https://openalex.org/I43313876"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5104433023"],"corresponding_institution_ids":["https://openalex.org/I43313876"],"apc_list":{"value":1350,"currency":"USD","value_usd":1350},"apc_paid":{"value":1350,"currency":"USD","value_usd":1350},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22014776,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"38","issue":"3","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9386000037193298,"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":0.9386000037193298,"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":0.01860000006854534,"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"}},{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.004399999976158142,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.6887999773025513},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.6503000259399414},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.6211000084877014},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.5910000205039978},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.46129998564720154},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.41940000653266907},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4146000146865845},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41110000014305115},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.40059998631477356}],"concepts":[{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.6887999773025513},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6503000259399414},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6211000084877014},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.609499990940094},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.5910000205039978},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.49390000104904175},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.46129998564720154},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42149999737739563},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.41940000653266907},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4146000146865845},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41280001401901245},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41110000014305115},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.40059998631477356},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3799999952316284},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.36160001158714294},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3479999899864197},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33799999952316284},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3334999978542328},{"id":"https://openalex.org/C203332170","wikidata":"https://www.wikidata.org/wiki/Q6334079","display_name":"Multivariate interpolation","level":3,"score":0.32739999890327454},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C2780505938","wikidata":"https://www.wikidata.org/wiki/Q17093282","display_name":"Unavailability","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.30250000953674316},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30219998955726624},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C171836373","wikidata":"https://www.wikidata.org/wiki/Q2266329","display_name":"Linear interpolation","level":3,"score":0.29440000653266907},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.29100000858306885},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2563999891281128}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s44443-026-00494-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44443-026-00494-z","pdf_url":null,"source":{"id":"https://openalex.org/S2764955546","display_name":"Journal of King Saud University - Computer and Information Sciences","issn_l":"1319-1578","issn":["1319-1578","2213-1248"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of King Saud University Computer and Information Sciences","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s44443-026-00494-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44443-026-00494-z","pdf_url":null,"source":{"id":"https://openalex.org/S2764955546","display_name":"Journal of King Saud University - Computer and Information Sciences","issn_l":"1319-1578","issn":["1319-1578","2213-1248"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of King Saud University Computer and Information Sciences","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1528741131","https://openalex.org/W1534477342","https://openalex.org/W1563938718","https://openalex.org/W1881549354","https://openalex.org/W1901616594","https://openalex.org/W2008056655","https://openalex.org/W2027461913","https://openalex.org/W2059700429","https://openalex.org/W2085281262","https://openalex.org/W2087240369","https://openalex.org/W2088252378","https://openalex.org/W2089471579","https://openalex.org/W2104167780","https://openalex.org/W2104933073","https://openalex.org/W2115969689","https://openalex.org/W2118978333","https://openalex.org/W2124710650","https://openalex.org/W2132791018","https://openalex.org/W2141014056","https://openalex.org/W2148143831","https://openalex.org/W2217007515","https://openalex.org/W2223903364","https://openalex.org/W2759373552","https://openalex.org/W2800788706","https://openalex.org/W2980634678","https://openalex.org/W3009384997","https://openalex.org/W3048804154","https://openalex.org/W3102927640","https://openalex.org/W4200122681","https://openalex.org/W4200494353","https://openalex.org/W4243150065","https://openalex.org/W4243610423","https://openalex.org/W4380609322","https://openalex.org/W4385695948","https://openalex.org/W4387432044","https://openalex.org/W4389965896","https://openalex.org/W4390659218","https://openalex.org/W4393982762","https://openalex.org/W4396574709","https://openalex.org/W4396898756","https://openalex.org/W4399734362","https://openalex.org/W4401431242","https://openalex.org/W4406261316","https://openalex.org/W4412934111"],"related_works":[],"abstract_inverted_index":{"In":[0,315],"machine":[1],"learning,":[2],"the":[3,34,40,49,74,93,104,157,188,198,201,213,229,232,239,259,270,306,312,320,324],"problem":[4],"of":[5,76,96,180,190,200,246,272,317,330],"class":[6],"imbalance":[7],"still":[8],"acts":[9],"as":[10],"a":[11,120,169,177,205],"bottleneck":[12],"that":[13,65,257],"limits":[14],"how":[15,72],"well":[16],"traditional":[17],"classifiers":[18],"perform.":[19],"There":[20],"are":[21,79,195,235,250],"two":[22],"main":[23],"angles":[24],"to":[25,31,52,80,118,137,144,197,212,238,252,293],"tackle":[26],"this":[27,166,225],"issue:":[28],"one":[29,81],"related":[30],"data":[32,41,54,138,146],"and":[33,140,147,155,216,243,286],"other":[35,303],"focused":[36],"on":[37,152,176,187,224,266,276],"algorithms.":[38],"At":[39],"processing":[42,233],"level,":[43],"various":[44],"sampling":[45,218],"methods":[46],"have":[47],"become":[48],"mainstream":[50],"means":[51],"balance":[53],"distribution,":[55],"among":[56],"which":[57,334],"SMOTE":[58,70,126],"stands":[59],"out":[60,329],"prominently.":[61],"Unlike":[62],"random":[63,109],"over-sampling":[64,173],"simply":[66],"duplicates":[67],"minority":[68,77,87,278],"samples,":[69],"assesses":[71],"similar":[73],"features":[75],"samples":[78,88,256,274,282],"another.":[82],"It":[83],"then":[84],"creates":[85],"brand-new":[86],"by":[89],"using":[90,283],"interpolation":[91,154,172,248],"within":[92],"feature":[94],"space":[95],"neighboring":[97],"samples.":[98],"This":[99],"approach":[100],"not":[101],"only":[102],"mitigates":[103],"overfitting":[105],"risk":[106],"inherent":[107],"in":[108,311,327,340],"oversampling":[110,304],"but":[111],"also":[112,127],"fully":[113,335],"leverages":[114,287],"existing":[115],"sample":[116],"information":[117,289],"construct":[119],"more":[121],"balanced":[122],"training":[123],"set.":[124],"However,":[125],"has":[128,134],"several":[129],"drawbacks.":[130],"For":[131],"example,":[132],"it":[133,150],"poor":[135],"adaptability":[136],"distributions":[139],"is":[141,209,221],"extremely":[142],"sensitive":[143],"noisy":[145],"outliers.":[148],"Additionally,":[149],"relies":[151],"pairwise":[153],"lacks":[156],"capability":[158],"for":[159,290],"dynamic":[160,171,217],"adjustment.":[161],"To":[162],"address":[163],"these":[164],"issues,":[165],"paper":[167],"proposes":[168],"novel":[170],"method":[174],"based":[175,223,275],"scoring":[178,207],"mechanism":[179,208],"regular":[181,191,202],"triangles":[182],"with":[183,302],"perturbations":[184,194],"(TSP-SMOTE).":[185],"First,":[186],"basis":[189],"triangle":[192],"tessellation,":[193],"applied":[196],"vertices":[199],"triangles.":[203],"Subsequently,":[204],"targeted":[206],"constructed":[210],"according":[211],"region":[214,291],"type,":[215],"point":[219],"selection":[220],"realized":[222],"mechanism.":[226],"After":[227],"completing":[228],"above":[230],"operations,":[231],"results":[234,298],"mapped":[236],"back":[237],"original":[240],"dimensional":[241],"space,":[242],"multiple":[244,284],"rounds":[245],"linear":[247],"operations":[249],"performed":[251],"finally":[253],"generate":[254],"new":[255,281],"meet":[258],"requirements.":[260],"The":[261,296],"TSP-SMOTE":[262,307,321],"algorithm":[263,308,322],"eliminates":[264],"reliance":[265],"k-nearest":[267],"neighbors,":[268],"adapts":[269],"number":[271],"synthetic":[273],"local":[277],"density,":[279],"synthesizes":[280],"instances,":[285],"all-class":[288],"construction":[292],"suppress":[294],"noise.":[295],"experimental":[297],"show":[299],"that,":[300],"compared":[301],"methods,":[305],"ranks":[309],"1st":[310],"average":[313],"ranking.":[314],"terms":[316],"classification":[318],"accuracy,":[319],"achieves":[323],"highest":[325],"value":[326],"118":[328],"all":[331],"234":[332],"metrics,":[333],"demonstrates":[336],"its":[337],"excellent":[338],"performance":[339],"addressing":[341],"class-imbalanced":[342],"problems.":[343]},"counts_by_year":[],"updated_date":"2026-04-04T06:10:10.580331","created_date":"2026-01-29T00:00:00"}
