{"id":"https://openalex.org/W7155368090","doi":"https://doi.org/10.31449/inf.v50i11.12882","title":"CAFWE: A Feature-Weighted Ensemble Classifier for Predicting Performance of Copper-Aluminum Composite Conductors","display_name":"CAFWE: A Feature-Weighted Ensemble Classifier for Predicting Performance of Copper-Aluminum Composite Conductors","publication_year":2026,"publication_date":"2026-04-23","ids":{"openalex":"https://openalex.org/W7155368090","doi":"https://doi.org/10.31449/inf.v50i11.12882"},"language":null,"primary_location":{"id":"doi:10.31449/inf.v50i11.12882","is_oa":true,"landing_page_url":"https://doi.org/10.31449/inf.v50i11.12882","pdf_url":"https://www.informatica.si/index.php/informatica/article/download/12882/6659","source":{"id":"https://openalex.org/S4210173311","display_name":"Informatica","issn_l":"0350-5596","issn":["0350-5596","1854-3871"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310314525","host_organization_name":"Slovenian Society Informatika","host_organization_lineage":["https://openalex.org/P4310314525"],"host_organization_lineage_names":["Slovenian Society Informatika"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Informatica","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.informatica.si/index.php/informatica/article/download/12882/6659","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134418444","display_name":"Yinghan Xiang","orcid":null},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinghan Xiang","raw_affiliation_strings":["China Electric Power Research Institute \uff0cBeijing 100192, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Electric Power Research Institute \uff0cBeijing 100192, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134441658","display_name":"Congrui Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210104681","display_name":"Tongliao Academy of Agricultural Sciences","ror":"https://ror.org/01c4gq057","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210104681"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Congrui Zhang","raw_affiliation_strings":["State Grid Tongliao Power Supply Company, Inner Mongolia Tongliao City 028000, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Tongliao Power Supply Company, Inner Mongolia Tongliao City 028000, China","institution_ids":["https://openalex.org/I4210104681"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134420741","display_name":"Shibo Hou","orcid":null},"institutions":[{"id":"https://openalex.org/I4210104681","display_name":"Tongliao Academy of Agricultural Sciences","ror":"https://ror.org/01c4gq057","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210104681"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shibo Hou","raw_affiliation_strings":["State Grid Tongliao Power Supply Company, Inner Mongolia Tongliao City 028000, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Tongliao Power Supply Company, Inner Mongolia Tongliao City 028000, China","institution_ids":["https://openalex.org/I4210104681"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134381620","display_name":"Yifu Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210104681","display_name":"Tongliao Academy of Agricultural Sciences","ror":"https://ror.org/01c4gq057","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210104681"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifu Wu","raw_affiliation_strings":["State Grid Tongliao Power Supply Company, Inner Mongolia Tongliao City 028000, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Tongliao Power Supply Company, Inner Mongolia Tongliao City 028000, China","institution_ids":["https://openalex.org/I4210104681"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5134435863","display_name":"Xu Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210104681","display_name":"Tongliao Academy of Agricultural Sciences","ror":"https://ror.org/01c4gq057","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210104681"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Guo","raw_affiliation_strings":["State Grid Tongliao Power Supply Company, Inner Mongolia Tongliao City 028000, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Tongliao Power Supply Company, Inner Mongolia Tongliao City 028000, China","institution_ids":["https://openalex.org/I4210104681"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.56151873,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"50","issue":"11","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10310","display_name":"Corrosion Behavior and Inhibition","score":0.2117999941110611,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10310","display_name":"Corrosion Behavior and Inhibition","score":0.2117999941110611,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12682","display_name":"Smart Materials for Construction","score":0.07800000160932541,"subfield":{"id":"https://openalex.org/subfields/2310","display_name":"Pollution"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10717","display_name":"Aluminum Alloys Composites Properties","score":0.055799998342990875,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6581000089645386},{"id":"https://openalex.org/keywords/electrical-conductor","display_name":"Electrical conductor","score":0.5626000165939331},{"id":"https://openalex.org/keywords/composite-number","display_name":"Composite number","score":0.46059998869895935},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4404999911785126},{"id":"https://openalex.org/keywords/conductor","display_name":"Conductor","score":0.4185999929904938},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40720000863075256},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.390500009059906}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6581000089645386},{"id":"https://openalex.org/C202374169","wikidata":"https://www.wikidata.org/wiki/Q124291","display_name":"Electrical conductor","level":2,"score":0.5626000165939331},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5077999830245972},{"id":"https://openalex.org/C104779481","wikidata":"https://www.wikidata.org/wiki/Q50707","display_name":"Composite number","level":2,"score":0.46059998869895935},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4528999924659729},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4404999911785126},{"id":"https://openalex.org/C34800285","wikidata":"https://www.wikidata.org/wiki/Q5159395","display_name":"Conductor","level":2,"score":0.4185999929904938},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40720000863075256},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.390500009059906},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.364300012588501},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36149999499320984},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.35409998893737793},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.3531999886035919},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.32589998841285706},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.3181999921798706},{"id":"https://openalex.org/C2777115002","wikidata":"https://www.wikidata.org/wiki/Q7168246","display_name":"Performance prediction","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.26930001378059387}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.31449/inf.v50i11.12882","is_oa":true,"landing_page_url":"https://doi.org/10.31449/inf.v50i11.12882","pdf_url":"https://www.informatica.si/index.php/informatica/article/download/12882/6659","source":{"id":"https://openalex.org/S4210173311","display_name":"Informatica","issn_l":"0350-5596","issn":["0350-5596","1854-3871"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310314525","host_organization_name":"Slovenian Society Informatika","host_organization_lineage":["https://openalex.org/P4310314525"],"host_organization_lineage_names":["Slovenian Society Informatika"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Informatica","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.31449/inf.v50i11.12882","is_oa":true,"landing_page_url":"https://doi.org/10.31449/inf.v50i11.12882","pdf_url":"https://www.informatica.si/index.php/informatica/article/download/12882/6659","source":{"id":"https://openalex.org/S4210173311","display_name":"Informatica","issn_l":"0350-5596","issn":["0350-5596","1854-3871"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310314525","host_organization_name":"Slovenian Society Informatika","host_organization_lineage":["https://openalex.org/P4310314525"],"host_organization_lineage_names":["Slovenian Society Informatika"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Informatica","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land","score":0.42313912510871887}],"awards":[],"funders":[{"id":"https://openalex.org/F4320335967","display_name":"Science and Technology Project of State Grid","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7155368090.pdf","grobid_xml":"https://content.openalex.org/works/W7155368090.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Copper-aluminum":[0],"composite":[1],"conductors":[2],"are":[3],"suitable":[4],"for":[5,254],"modern":[6],"electrical":[7,13,26],"applications":[8],"due":[9],"to":[10,118,145,231,249],"their":[11,20],"high":[12],"conductivity":[14],"and":[15,25,30,49,57,84,103,121,136,168,205,216,224,238,244],"lightweight":[16],"nature;":[17],"however,":[18],"predicting":[19],"performance":[21,63],"in":[22],"changing":[23],"environmental":[24],"conditions":[27],"is":[28],"difficult":[29],"expensive":[31],"using":[32,74,131],"conventional":[33],"techniques.":[34],"Problem":[35],"Statement:":[36],"Previous":[37],"models":[38],"ignore":[39],"the":[40,75,227],"combined":[41,108],"effects":[42],"of":[43,192],"copper-aluminum":[44,241],"ratio,":[45],"tensile":[46],"strength,":[47],"temperature,":[48],"current":[50],"load;":[51],"furthermore,":[52],"metrics":[53,151],"such":[54],"as":[55,226],"accuracy":[56],"F1-score":[58],"cannot":[59],"accurately":[60,237],"reflect":[61],"physical":[62],"characteristics.":[64],"A":[65],"new":[66],"Copper-Aluminum":[67],"Feature-Weighted":[68],"Ensemble":[69],"(CAFWE)":[70],"classifier":[71,106],"was":[72,129],"developed":[73],"CopperAluminum_WirePerformance_Dataset":[76],"containing":[77],"5000":[78],"samples":[79],"with":[80,175,189],"10":[81],"input":[82],"features":[83],"one":[85],"target":[86],"output":[87],"(performance_level).":[88],"The":[89,127,234],"model":[90,236],"integrates":[91],"three":[92],"base":[93],"learners":[94],"\u2014":[95,107],"Linear":[96],"Classifier,":[97],"k-Nearest":[98],"Neighbor":[99],"(k":[100],"=":[101,194,197,200,203,207],"5),":[102],"a":[104,110,246],"Decision-Stump":[105],"through":[109],"weighted":[111],"voting":[112],"mechanism":[113],"that":[114],"assigns":[115],"higher":[116],"weights":[117],"copper_percentage,":[119],"tensile_strength,":[120],"electrical_conductivity":[122,225],"based":[123],"on":[124],"feature-sensitivity":[125],"analysis.":[126],"dataset":[128],"partitioned":[130],"an":[132],"80:20":[133],"stratified":[134],"split,":[135],"all":[137],"results":[138],"were":[139,152],"averaged":[140],"over":[141],"five":[142,181],"repeated":[143],"experiments":[144],"ensure":[146],"stability.":[147],"Five":[148],"domain-specific":[149],"evaluation":[150],"introduced:":[153],"Conductivity":[154],"Accuracy":[155],"(CA),":[156],"Strength":[157],"Reliability":[158],"(SR),":[159],"Temperature":[160],"Adaptation":[161],"Score":[162],"(TAS),":[163],"Load":[164],"Prediction":[165],"Stability":[166],"(LPS),":[167],"Composite":[169],"Material":[170],"Alignment":[171],"(CMA),":[172],"enabling":[173],"alignment":[174],"real-world":[176],"engineering":[177],"behavior.":[178],"Results:":[179],"Across":[180],"independent":[182],"training":[183],"runs,":[184],"CAFWE":[185,235],"achieved":[186],"consistent":[187],"performance,":[188],"mean":[190],"scores":[191],"CA":[193],"93.5%,":[195],"SR":[196],"91.2%,":[198],"TAS":[199],"89.8%,":[201],"LPS":[202],"90.6%,":[204],"CMA":[206],"92.4%,":[208],"demonstrating":[209],"superior":[210],"predictive":[211],"reliability":[212],"under":[213],"varying":[214],"material":[215,252],"operational":[217],"conditions.":[218],"Feature":[219],"importance":[220],"analysis":[221],"confirmed":[222],"copper_percentage":[223],"most":[228],"influential":[229],"contributors":[230],"final":[232],"predictions.":[233],"interpretably":[239],"predicts":[240],"conductor":[242],"performance;":[243],"provides":[245],"scalable":[247],"framework":[248],"optimize":[250],"hybrid":[251],"design":[253],"smart":[255],"grid":[256],"applications.":[257]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-24T00:00:00"}