{"id":"https://openalex.org/W3089802039","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207177","title":"High-Level Classification for Multi-Label Learning","display_name":"High-Level Classification for Multi-Label Learning","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3089802039","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207177","mag":"3089802039"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207177","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207177","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","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/A5087045761","display_name":"Vin\u00edcius H. Resende","orcid":null},"institutions":[{"id":"https://openalex.org/I80850581","display_name":"Universidade Federal de Uberl\u00e2ndia","ror":"https://ror.org/04x3wvr31","country_code":"BR","type":"education","lineage":["https://openalex.org/I80850581"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Vinicius H. Resende","raw_affiliation_strings":["Faculty of Computing, Federal University of Uberl\u00e2ndia, Uberl\u00e2ndia, Brazil"],"affiliations":[{"raw_affiliation_string":"Faculty of Computing, Federal University of Uberl\u00e2ndia, Uberl\u00e2ndia, Brazil","institution_ids":["https://openalex.org/I80850581"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033849591","display_name":"Murillo G. Carneiro","orcid":"https://orcid.org/0000-0002-2915-8990"},"institutions":[{"id":"https://openalex.org/I80850581","display_name":"Universidade Federal de Uberl\u00e2ndia","ror":"https://ror.org/04x3wvr31","country_code":"BR","type":"education","lineage":["https://openalex.org/I80850581"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Murillo G. Carneiro","raw_affiliation_strings":["Faculty of Computing, Federal University of Uberl\u00e2ndia, Uberl\u00e2ndia, Brazil"],"affiliations":[{"raw_affiliation_string":"Faculty of Computing, Federal University of Uberl\u00e2ndia, Uberl\u00e2ndia, Brazil","institution_ids":["https://openalex.org/I80850581"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5087045761"],"corresponding_institution_ids":["https://openalex.org/I80850581"],"apc_list":null,"apc_paid":null,"fwci":0.3977,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.69021793,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"8","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9998999834060669,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9998999834060669,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.986299991607666,"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/T11644","display_name":"Spam and Phishing Detection","score":0.9848999977111816,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/computer-science","display_name":"Computer science","score":0.7650070190429688},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.6969885230064392},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6005100607872009},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5856962203979492},{"id":"https://openalex.org/keywords/multi-label-classification","display_name":"Multi-label classification","score":0.5299233794212341},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5110155344009399},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45020192861557007}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7650070190429688},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.6969885230064392},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6005100607872009},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5856962203979492},{"id":"https://openalex.org/C2776482837","wikidata":"https://www.wikidata.org/wiki/Q3553958","display_name":"Multi-label classification","level":2,"score":0.5299233794212341},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5110155344009399},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45020192861557007},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9207177","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207177","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W55768394","https://openalex.org/W1565746575","https://openalex.org/W1905051261","https://openalex.org/W1989492902","https://openalex.org/W1999954155","https://openalex.org/W2011972438","https://openalex.org/W2052684427","https://openalex.org/W2077551920","https://openalex.org/W2114315281","https://openalex.org/W2118712128","https://openalex.org/W2119466907","https://openalex.org/W2146241755","https://openalex.org/W2156935079","https://openalex.org/W2165766797","https://openalex.org/W2553731860","https://openalex.org/W2741069800","https://openalex.org/W2754969470","https://openalex.org/W2904486267","https://openalex.org/W2905073742","https://openalex.org/W3006060200","https://openalex.org/W6677758222","https://openalex.org/W6683944004"],"related_works":["https://openalex.org/W2329500892","https://openalex.org/W28991112","https://openalex.org/W2370726991","https://openalex.org/W2369710579","https://openalex.org/W4327728159","https://openalex.org/W2912751582","https://openalex.org/W1990856605","https://openalex.org/W2053783616","https://openalex.org/W2545348020","https://openalex.org/W4281776617"],"abstract_inverted_index":{"Multi-label":[0],"learning":[1,7,82],"(MLL)":[2],"addresses":[3],"the":[4,46,50,58,68,72,89,95,99,109,138,154,158],"problem":[5],"of":[6,25,49,98,141,157],"from":[8,108],"data":[9,52,69,151],"items":[10],"which":[11,113,127],"can":[12],"be":[13],"associated":[14],"with":[15,170],"multiple":[16],"labels":[17],"simultaneously.":[18],"As":[19],"MLL":[20,36,125,143,159,173],"techniques":[21,37,133],"are":[22],"usually":[23],"derived":[24],"single-label":[26],"ones,":[27],"they":[28,43],"also":[29,102,162],"share":[30],"common":[31],"drawbacks.":[32],"For":[33],"example,":[34],"most":[35],"perform":[38],"a":[39,124],"low-level":[40,130],"classification,":[41],"i.e.,":[42],"consider":[44,92],"only":[45,94],"physical":[47,96],"features":[48,97,106,156],"input":[51],"(e.g.,":[53],"distance,":[54],"distribution,":[55],"etc)":[56],"in":[57,134,168],"classification":[59],"process,":[60],"having":[61],"troubles":[62],"to":[63,91,136],"detect":[64],"semantic":[65],"relationships":[66],"among":[67],"items,":[70],"like":[71],"formation":[73],"pattern":[74],"for":[75],"example.":[76],"Recent":[77],"studies":[78],"have":[79,88],"shown":[80],"that":[81,176],"systems":[83],"based":[84],"on":[85,147],"complex":[86],"networks":[87],"ability":[90],"not":[93],"data,":[100],"but":[101],"structural":[103],"and":[104,131,149,161],"topological":[105],"extracted":[107],"network":[110],"connection":[111],"patterns,":[112],"is":[114],"known":[115],"as":[116],"high-level":[117,132],"classification.":[118],"In":[119],"this":[120],"paper,":[121],"we":[122],"investigate":[123],"framework":[126,160,178],"combines":[128],"both":[129],"order":[135],"improve":[137,181],"predictive":[139,166,183],"performance":[140,167],"existing":[142],"techniques.":[144],"Experiments":[145],"conducted":[146],"artificial":[148],"real-world":[150],"sets":[152],"highlighted":[153],"salient":[155],"attested":[163],"its":[164],"good":[165],"comparison":[169],"widely":[171],"used":[172],"techniques,":[174],"indicating":[175],"our":[177],"may":[179],"considerable":[180],"their":[182],"performance.":[184]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
