{"id":"https://openalex.org/W2977691589","doi":"https://doi.org/10.1109/ijcnn.2019.8852454","title":"Multiple-Instance Learning through Optimum-Path Forest","display_name":"Multiple-Instance Learning through Optimum-Path Forest","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2977691589","doi":"https://doi.org/10.1109/ijcnn.2019.8852454","mag":"2977691589"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852454","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852454","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 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/A5078890782","display_name":"Luis C. S. Afonso","orcid":"https://orcid.org/0000-0002-5543-3896"},"institutions":[{"id":"https://openalex.org/I177909021","display_name":"Universidade Federal de S\u00e3o Carlos","ror":"https://ror.org/00qdc6m37","country_code":"BR","type":"education","lineage":["https://openalex.org/I177909021"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Luis C. S. Afonso","raw_affiliation_strings":["Department of Computing, UFSCar - Federal University of S\u00e3o Carlos, S\u00e3o Carlos, Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Computing, UFSCar - Federal University of S\u00e3o Carlos, S\u00e3o Carlos, Brazil","institution_ids":["https://openalex.org/I177909021"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016083289","display_name":"Danilo Colombo","orcid":"https://orcid.org/0000-0001-5832-7573"},"institutions":[{"id":"https://openalex.org/I177909021","display_name":"Universidade Federal de S\u00e3o Carlos","ror":"https://ror.org/00qdc6m37","country_code":"BR","type":"education","lineage":["https://openalex.org/I177909021"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Danilo Colombo","raw_affiliation_strings":["Department of Computing, UFSCar - Federal University of S\u00e3o Carlos, S\u00e3o Carlos, Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Computing, UFSCar - Federal University of S\u00e3o Carlos, S\u00e3o Carlos, Brazil","institution_ids":["https://openalex.org/I177909021"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031485083","display_name":"Clayton R. Pereira","orcid":"https://orcid.org/0000-0002-0427-4880"},"institutions":[{"id":"https://openalex.org/I32393484","display_name":"Petrobras (Brazil)","ror":"https://ror.org/0235kyq22","country_code":"BR","type":"company","lineage":["https://openalex.org/I32393484"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Clayton R. Pereira","raw_affiliation_strings":["Cenpes, Petr\u00f3leo Brasileiro S.A., Rio de Janeiro - RJ, Brazil"],"affiliations":[{"raw_affiliation_string":"Cenpes, Petr\u00f3leo Brasileiro S.A., Rio de Janeiro - RJ, Brazil","institution_ids":["https://openalex.org/I32393484"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000302987","display_name":"Kelton Augusto Pontara da Costa","orcid":"https://orcid.org/0000-0001-5458-3908"},"institutions":[{"id":"https://openalex.org/I879563668","display_name":"Universidade Estadual Paulista (Unesp)","ror":"https://ror.org/00987cb86","country_code":"BR","type":"education","lineage":["https://openalex.org/I879563668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Kelton A. P. Costa","raw_affiliation_strings":["Department of Computing, UNESP \u2013 S\u00e3o Paulo State University, Bauru, Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Computing, UNESP \u2013 S\u00e3o Paulo State University, Bauru, Brazil","institution_ids":["https://openalex.org/I879563668"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003275797","display_name":"Jo\u00e3o Paulo Papa","orcid":"https://orcid.org/0000-0002-6494-7514"},"institutions":[{"id":"https://openalex.org/I879563668","display_name":"Universidade Estadual Paulista (Unesp)","ror":"https://ror.org/00987cb86","country_code":"BR","type":"education","lineage":["https://openalex.org/I879563668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Joao P. Papa","raw_affiliation_strings":["Department of Computing, UNESP \u2013 S\u00e3o Paulo State University, Bauru, Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Computing, UNESP \u2013 S\u00e3o Paulo State University, Bauru, Brazil","institution_ids":["https://openalex.org/I879563668"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5078890782"],"corresponding_institution_ids":["https://openalex.org/I177909021"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09740606,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"abs 1607 1759","issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13429","display_name":"Electricity Theft Detection Techniques","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13429","display_name":"Electricity Theft Detection Techniques","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9519000053405762,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.9426000118255615,"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/computer-science","display_name":"Computer science","score":0.696922242641449},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6775742173194885},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.6060322523117065},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6046937108039856},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.5463911890983582},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5356855392456055},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.510868489742279},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5063797235488892},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.445687860250473},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.44431281089782715},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.41898152232170105},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35857170820236206},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.19667398929595947}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.696922242641449},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6775742173194885},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.6060322523117065},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6046937108039856},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.5463911890983582},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5356855392456055},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.510868489742279},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5063797235488892},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.445687860250473},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.44431281089782715},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.41898152232170105},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35857170820236206},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.19667398929595947},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8852454","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852454","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W24402856","https://openalex.org/W1524408959","https://openalex.org/W1540386283","https://openalex.org/W1544144649","https://openalex.org/W2004954930","https://openalex.org/W2047822984","https://openalex.org/W2087852829","https://openalex.org/W2096411924","https://openalex.org/W2108745803","https://openalex.org/W2110119381","https://openalex.org/W2115240329","https://openalex.org/W2115284428","https://openalex.org/W2125479168","https://openalex.org/W2158769954","https://openalex.org/W2163474322","https://openalex.org/W2398616336","https://openalex.org/W2512089339","https://openalex.org/W2571156573","https://openalex.org/W2811215564","https://openalex.org/W2897461699","https://openalex.org/W2898008594","https://openalex.org/W2963626623","https://openalex.org/W3120740533","https://openalex.org/W4248916828","https://openalex.org/W4250320908","https://openalex.org/W6600987510","https://openalex.org/W6632200337","https://openalex.org/W6676245398","https://openalex.org/W6677492792","https://openalex.org/W6684158799","https://openalex.org/W6719819555"],"related_works":["https://openalex.org/W2165912799","https://openalex.org/W2735662278","https://openalex.org/W2382615723","https://openalex.org/W4311804456","https://openalex.org/W1987484445","https://openalex.org/W2623658258","https://openalex.org/W2143413548","https://openalex.org/W4382934300","https://openalex.org/W2121061354","https://openalex.org/W4285388059"],"abstract_inverted_index":{"Multiple-instance":[0],"(MI)":[1],"learning":[2,27,82,124],"aims":[3],"at":[4],"modeling":[5],"problems":[6,120],"that":[7,91,109],"are":[8,114],"better":[9],"described":[10],"by":[11,24],"several":[12],"instances":[13],"of":[14,19,80],"a":[15,52],"given":[16],"sample":[17,43],"instead":[18],"individual":[20],"descriptions":[21],"often":[22],"employed":[23],"standard":[25],"machine":[26],"approaches.":[28],"In":[29,67],"binary-driven":[30],"MI":[31],"problems,":[32],"the":[33,49,72,78,122],"entire":[34],"bag":[35,53],"is":[36,44,54],"considered":[37,55],"positive":[38],"if":[39,57],"one":[40],"(at":[41],"least)":[42],"labeled":[45,62],"as":[46,63,65],"positive.":[47],"On":[48],"other":[50],"hand,":[51],"negative":[56,64],"it":[58,87],"contains":[59],"all":[60],"samples":[61],"well.":[66],"this":[68],"paper,":[69],"we":[70,85],"introduced":[71],"Optimum-Path":[73],"Forest":[74],"(OPF)":[75],"classifier":[76],"to":[77,118],"context":[79],"multiple-instance":[81,123],"paradigm,":[83],"and":[84,98],"evaluated":[86],"in":[88,101,121],"different":[89,111],"scenarios":[90],"range":[92],"from":[93],"molecule":[94],"description,":[95],"text":[96],"categorization,":[97],"anomaly":[99],"detection":[100],"well-drilling":[102],"report":[103],"classification.":[104],"The":[105],"experimental":[106],"results":[107],"showed":[108],"two":[110],"OPF":[112],"classifiers":[113],"very":[115],"much":[116],"suitable":[117],"handle":[119],"paradigm.":[125]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}