{"id":"https://openalex.org/W3089801830","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206755","title":"An Optimized Modularity-Based High Level Classification Model","display_name":"An Optimized Modularity-Based High Level Classification Model","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3089801830","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206755","mag":"3089801830"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9206755","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206755","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/A5011782492","display_name":"Tiago Colliri","orcid":"https://orcid.org/0000-0001-9233-4662"},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Tiago Colliri","raw_affiliation_strings":["Dept. of Computer Science, ICMC - University of Sao Paulo, Sao Carlos, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, ICMC - University of Sao Paulo, Sao Carlos, Brazil","institution_ids":["https://openalex.org/I17974374"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100393230","display_name":"Weiguang Liu","orcid":"https://orcid.org/0000-0002-1899-9495"},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Weiguang Liu","raw_affiliation_strings":["Dept. of Computer Science, ICMC - University of Sao Paulo, Sao Carlos, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, ICMC - University of Sao Paulo, Sao Carlos, Brazil","institution_ids":["https://openalex.org/I17974374"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100697945","display_name":"Liang Zhao","orcid":"https://orcid.org/0000-0002-1502-6604"},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Liang Zhao","raw_affiliation_strings":["Dept. of Computer Science, ICMC - University of Sao Paulo, Sao Carlos, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, ICMC - University of Sao Paulo, Sao Carlos, Brazil","institution_ids":["https://openalex.org/I17974374"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I17974374"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11851145,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9904000163078308,"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/T10320","display_name":"Neural Networks and Applications","score":0.9904000163078308,"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/T10211","display_name":"Computational Drug Discovery Methods","score":0.9879000186920166,"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"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9851999878883362,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.7781067490577698},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6658896207809448},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6583261489868164},{"id":"https://openalex.org/keywords/modularity","display_name":"Modularity (biology)","score":0.6260029077529907},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.568810224533081},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5308324694633484},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.49301907420158386},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4890912175178528},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43754395842552185},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4290153682231903},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3301975727081299}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7781067490577698},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6658896207809448},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6583261489868164},{"id":"https://openalex.org/C2779478453","wikidata":"https://www.wikidata.org/wiki/Q6889748","display_name":"Modularity (biology)","level":2,"score":0.6260029077529907},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.568810224533081},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5308324694633484},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.49301907420158386},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4890912175178528},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43754395842552185},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4290153682231903},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3301975727081299},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9206755","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206755","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":33,"referenced_works":["https://openalex.org/W179179905","https://openalex.org/W1554338108","https://openalex.org/W1968686485","https://openalex.org/W1976969221","https://openalex.org/W1981457167","https://openalex.org/W1988790447","https://openalex.org/W1994476682","https://openalex.org/W2013614800","https://openalex.org/W2047028564","https://openalex.org/W2047940964","https://openalex.org/W2071436088","https://openalex.org/W2077551920","https://openalex.org/W2101234009","https://openalex.org/W2120665364","https://openalex.org/W2124637492","https://openalex.org/W2125283600","https://openalex.org/W2139755242","https://openalex.org/W2156909104","https://openalex.org/W2895792207","https://openalex.org/W2897130202","https://openalex.org/W2911964244","https://openalex.org/W2912934387","https://openalex.org/W2984605817","https://openalex.org/W3022436500","https://openalex.org/W3120740533","https://openalex.org/W4212883601","https://openalex.org/W4230674625","https://openalex.org/W4248437541","https://openalex.org/W4255455317","https://openalex.org/W6607259140","https://openalex.org/W6642552204","https://openalex.org/W6675354045","https://openalex.org/W6680452806"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4230687177","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W1968829728","https://openalex.org/W2349859869","https://openalex.org/W2130974462","https://openalex.org/W4316651471","https://openalex.org/W2028665553","https://openalex.org/W3120118008"],"abstract_inverted_index":{"In":[0,73,112],"this":[1,66,113,119,152],"paper,":[2],"we":[3,75],"introduce":[4,77],"a":[5,17,20,32,55,78,83,109],"network-based":[6],"classification":[7,167,180],"model":[8,38,157,194],"which,":[9],"instead":[10],"of":[11,58,65,127,136],"mapping":[12],"each":[13,26],"data":[14,27],"instance":[15,28],"as":[16,22,30],"node":[18],"in":[19,90,134],"network,":[21,50],"usual,":[23],"it":[24,160],"maps":[25],"attribute":[29],"being":[31,195],"node.":[33],"This":[34],"procedure":[35],"allows":[36,141],"the":[37,44,49,70,91,95,103,106,115,125,131,142,146,156,183,192,201,206],"to":[39,62,81,124,144,161,174],"preserve":[40],"more":[41,122],"information":[42,68],"from":[43,118],"input":[45],"dataset":[46,92],"when":[47],"building":[48],"specially":[51],"for":[52,101],"datasets":[53],"with":[54,85,191],"larger":[56],"number":[57],"features,":[59],"and":[60,164,169],"thus":[61],"make":[63],"use":[64],"extra":[67],"during":[69,130],"training":[71],"phase.":[72],"addition,":[74],"also":[76],"technique":[79],"intended":[80],"generate":[82],"network":[84,116],"one":[86],"component":[87],"per":[88],"class":[89],"while":[93],"keeping":[94],"threshold":[96],"parameter,":[97],"which":[98,140],"is":[99,121],"responsible":[100],"determining":[102],"edges":[104],"among":[105,200],"nodes,":[107],"at":[108],"minimum":[110],"value.":[111],"way,":[114],"emerging":[117],"process":[120],"sensitive":[123],"insertion":[126],"new":[128,147],"instances,":[129],"testing":[132],"phase,":[133],"terms":[135],"its":[137,171],"modularity":[138],"measure,":[139],"classifier":[143],"infer":[145],"labels":[148],"based":[149],"mainly":[150],"on":[151,182,197,205],"measure.":[153],"We":[154],"evaluate":[155],"by":[158,177],"applying":[159],"both":[162],"artificial":[163],"real":[165],"benchmark":[166],"datasets,":[168],"have":[170],"performance":[172],"compared":[173],"those":[175],"obtained":[176],"other":[178],"traditional":[179],"models":[181],"same":[184],"data.":[185],"The":[186],"preliminary":[187],"results":[188],"are":[189],"encouraging,":[190],"proposed":[193],"ranked":[196],"second":[198],"place":[199],"10":[202],"classifiers":[203],"considered,":[204],"selected":[207],"datasets.":[208]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
