{"id":"https://openalex.org/W1989249186","doi":"https://doi.org/10.1109/ijcnn.2013.6707035","title":"A quasi-linear SVM combined with assembled SMOTE for imbalanced data classification","display_name":"A quasi-linear SVM combined with assembled SMOTE for imbalanced data classification","publication_year":2013,"publication_date":"2013-08-01","ids":{"openalex":"https://openalex.org/W1989249186","doi":"https://doi.org/10.1109/ijcnn.2013.6707035","mag":"1989249186"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2013.6707035","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2013.6707035","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101442102","display_name":"Bo Zhou","orcid":"https://orcid.org/0000-0002-7824-3673"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Bo Zhou","raw_affiliation_strings":["Graduate school of Information Production and Systems, Waseda University of Hibikino, Fukuoka, Japan","Grad. Sch. of Inf., Production & Syst., Waseda Univ. of Hibikino, Kitakyushu, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate school of Information Production and Systems, Waseda University of Hibikino, Fukuoka, Japan","institution_ids":["https://openalex.org/I150744194"]},{"raw_affiliation_string":"Grad. Sch. of Inf., Production & Syst., Waseda Univ. of Hibikino, Kitakyushu, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017431996","display_name":"Cheng Yang","orcid":"https://orcid.org/0000-0002-3858-1328"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Cheng Yang","raw_affiliation_strings":["Graduate school of Information Production and Systems, Waseda University of Hibikino, Fukuoka, Japan","Grad. Sch. of Inf., Production & Syst., Waseda Univ. of Hibikino, Kitakyushu, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate school of Information Production and Systems, Waseda University of Hibikino, Fukuoka, Japan","institution_ids":["https://openalex.org/I150744194"]},{"raw_affiliation_string":"Grad. Sch. of Inf., Production & Syst., Waseda Univ. of Hibikino, Kitakyushu, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100441408","display_name":"Haixiang Guo","orcid":"https://orcid.org/0000-0002-4274-3975"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haixiang Guo","raw_affiliation_strings":["School of Economics and Management, China University of Geosciences, Wuhan, Hubei, China","Sch. of Econ. & Manage., China Univ. of Geosci., Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Economics and Management, China University of Geosciences, Wuhan, Hubei, China","institution_ids":["https://openalex.org/I3124059619"]},{"raw_affiliation_string":"Sch. of Econ. & Manage., China Univ. of Geosci., Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100326923","display_name":"Jinglu Hu","orcid":"https://orcid.org/0000-0002-5601-7261"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jinglu Hu","raw_affiliation_strings":["Graduate school of Information Production and Systems, Waseda University of Hibikino, Fukuoka, Japan","Grad. Sch. of Inf., Production & Syst., Waseda Univ. of Hibikino, Kitakyushu, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate school of Information Production and Systems, Waseda University of Hibikino, Fukuoka, Japan","institution_ids":["https://openalex.org/I150744194"]},{"raw_affiliation_string":"Grad. Sch. of Inf., Production & Syst., Waseda Univ. of Hibikino, Kitakyushu, Japan","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9152,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.73737336,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9994000196456909,"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":0.9994000196456909,"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":0.9876999855041504,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9815000295639038,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.8010697960853577},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7977193593978882},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5856066942214966},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5819143652915955},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.55935138463974},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5345175862312317},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.48899662494659424},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.48338833451271057},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.45609840750694275},{"id":"https://openalex.org/keywords/decision-boundary","display_name":"Decision boundary","score":0.4195362329483032},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3913698196411133}],"concepts":[{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.8010697960853577},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7977193593978882},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5856066942214966},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5819143652915955},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.55935138463974},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5345175862312317},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.48899662494659424},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.48338833451271057},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.45609840750694275},{"id":"https://openalex.org/C42023084","wikidata":"https://www.wikidata.org/wiki/Q5249231","display_name":"Decision boundary","level":3,"score":0.4195362329483032},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3913698196411133},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2013.6707035","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2013.6707035","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/1","display_name":"No poverty","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1574715085","https://openalex.org/W1876437670","https://openalex.org/W1965680834","https://openalex.org/W1969887109","https://openalex.org/W1976496412","https://openalex.org/W1993119218","https://openalex.org/W1993332216","https://openalex.org/W2023639956","https://openalex.org/W2038221881","https://openalex.org/W2104933073","https://openalex.org/W2113188697","https://openalex.org/W2118978333","https://openalex.org/W2132791018","https://openalex.org/W2137029138","https://openalex.org/W2148143831","https://openalex.org/W2153635508","https://openalex.org/W2165828254","https://openalex.org/W4230674625","https://openalex.org/W4255306344","https://openalex.org/W6634333142","https://openalex.org/W6639459099","https://openalex.org/W6644299198","https://openalex.org/W6675634716","https://openalex.org/W6676991849","https://openalex.org/W6684893555"],"related_works":["https://openalex.org/W2766503024","https://openalex.org/W2781247653","https://openalex.org/W4206637278","https://openalex.org/W184442817","https://openalex.org/W2009611918","https://openalex.org/W2814966788","https://openalex.org/W1983643426","https://openalex.org/W4200464085","https://openalex.org/W4293162767","https://openalex.org/W4224061426"],"abstract_inverted_index":{"This":[0],"paper":[1,37],"focuses":[2],"on":[3,95,160],"imbalanced":[4],"dataset":[5],"classification":[6,139,175],"problem":[7,35],"by":[8,65,151],"using":[9,118,152],"SVM":[10,30,44,50,53,136,150],"and":[11,45,83,163,173],"oversampling":[12,15,75],"method.":[13],"Traditional":[14],"method":[16,41,93,170],"increases":[17],"the":[18,79,119,125,134,144,168],"occurrence":[19,85],"of":[20,29,42,78,86,107,121,140],"over-lapping":[21],"between":[22,88],"classes,":[23],"which":[24,108],"leads":[25],"to":[26,101],"poor":[27],"generalization":[28],"classification.":[31],"To":[32],"solve":[33],"this":[34,36],"proposes":[38],"a":[39,91,138,148,153],"combined":[40],"quasi-linear":[43,49,55,135,155],"assembled":[46,72,126],"SMOTE.":[47],"The":[48,71],"is":[51,99,171],"an":[52,60],"with":[54,69,76,112],"kernel":[56,156],"function.":[57,157],"It":[58],"realizes":[59,137],"approximate":[61],"nonlinear":[62],"separation":[63,115],"boundary":[64],"mulit-local":[66],"linear":[67,104,114,123],"boundaries":[68],"interpolation.":[70],"SMOTE":[73,127],"implements":[74],"considering":[77],"data":[80,162],"distribution":[81],"information":[82,120],"avoids":[84],"overlapping":[87],"classes.":[89],"Firstly,":[90],"partition":[92],"based":[94],"Minimal":[96],"Spanning":[97],"Tree":[98],"proposed":[100,169],"obtain":[102],"local":[103,122],"partitions,":[105,124],"each":[106],"can":[109],"be":[110],"separated":[111],"one":[113],"boundary.":[116],"Secondly,":[117],"generates":[128],"synthetic":[129],"minority":[130],"class":[131],"samples.":[132],"Finally,":[133],"oversampled":[141],"datasets":[142,165],"in":[143],"same":[145],"way":[146],"as":[147],"standard":[149],"composite":[154],"Experiment":[158],"results":[159],"artificial":[161],"benchmark":[164],"show":[166],"that":[167],"effective":[172],"improves":[174],"performances.":[176]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
