{"id":"https://openalex.org/W6944971542","doi":"https://doi.org/10.21227/7pf8-nq83","title":"Binary classifiers' outputs for ensemble creation","display_name":"Binary classifiers' outputs for ensemble creation","publication_year":2019,"publication_date":"2019-05-31","ids":{"openalex":"https://openalex.org/W6944971542","doi":"https://doi.org/10.21227/7pf8-nq83"},"language":"en","primary_location":{"id":"doi:10.21227/7pf8-nq83","is_oa":true,"landing_page_url":"https://doi.org/10.21227/7pf8-nq83","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.21227/7pf8-nq83","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Tiba, Attila","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tiba, Attila","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Hajdu, Andras","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hajdu, Andras","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Toman, Henrietta","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Toman, Henrietta","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Terdik, Gyorgy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Terdik, Gyorgy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.17679999768733978,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.17679999768733978,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"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/T10895","display_name":"Species Distribution and Climate Change","score":0.07360000163316727,"subfield":{"id":"https://openalex.org/subfields/2302","display_name":"Ecological Modeling"},"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/T13568","display_name":"Wood and Agarwood Research","score":0.060600001364946365,"subfield":{"id":"https://openalex.org/subfields/1605","display_name":"Organic Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/knapsack-problem","display_name":"Knapsack problem","score":0.7134000062942505},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6772000193595886},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.5874999761581421},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5120000243186951},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5080999732017517},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49219998717308044},{"id":"https://openalex.org/keywords/random-subspace-method","display_name":"Random subspace method","score":0.4867999851703644},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.48179998993873596}],"concepts":[{"id":"https://openalex.org/C113138325","wikidata":"https://www.wikidata.org/wiki/Q864457","display_name":"Knapsack problem","level":2,"score":0.7134000062942505},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6772000193595886},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6557999849319458},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6198999881744385},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5874999761581421},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5120000243186951},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5080999732017517},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49219998717308044},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.4867999851703644},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.48179998993873596},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.47769999504089355},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4553000032901764},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.43849998712539673},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.41600000858306885},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.3903000056743622},{"id":"https://openalex.org/C47702885","wikidata":"https://www.wikidata.org/wiki/Q5441227","display_name":"Feedforward neural network","level":3,"score":0.39010000228881836},{"id":"https://openalex.org/C3309909","wikidata":"https://www.wikidata.org/wiki/Q864155","display_name":"Binary decision diagram","level":2,"score":0.3776000142097473},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35019999742507935},{"id":"https://openalex.org/C192576344","wikidata":"https://www.wikidata.org/wiki/Q194706","display_name":"Boltzmann machine","level":3,"score":0.3402000069618225},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27300000190734863},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25600001215934753},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21227/7pf8-nq83","is_oa":true,"landing_page_url":"https://doi.org/10.21227/7pf8-nq83","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"dataset"}],"best_oa_location":{"id":"doi:10.21227/7pf8-nq83","is_oa":true,"landing_page_url":"https://doi.org/10.21227/7pf8-nq83","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"dataset"},"sustainable_development_goals":[{"score":0.7288922667503357,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"dataset":[1],"was":[2],"created":[3],"based":[4],"on":[5,87,125],"the":[6,24,57,100,119,128,132],"paper":[7],"'Andras":[8],"Hajdu,":[9],"Gyorgy":[10],"Terdik,":[11],"Attila":[12],"Tiba,":[13],"and":[14,69,108],"Henrietta":[15],"Toman:A":[16],"stochastic":[17],"approach":[18],"to":[19,77,80,106],"handle":[20],"knapsack":[21],"problems":[22],"in":[23],"creation":[25],"of":[26,72,84,96,112,127,131],"ensembles'.To":[27],"summarize":[28],"our":[29,91],"experimental":[30],"setup":[31],"for":[32,56,93],"UCI":[33,59],"binary":[34],"classification":[35],"problems,":[36],"we":[37],"have":[38],"considered":[39],"baseclassifiers":[40],"perceptron,":[41],"decision":[42],"tree,":[43],"Levenberg-Marquardt":[44],"feedforward":[45],"neural":[46,49],"network,":[47],"random":[48],"network,and":[50],"discriminative":[51],"restricted":[52],"Boltzmann":[53],"machine":[54],"classifier":[55],"5":[58],"datasets":[60,71],"MAGIC":[61],"Gamma":[62],"Telescope,":[63],"HIGGS,":[64],"EEG":[65],"EyeState,Musk":[66],"(Version":[67],"2),":[68],"Spambase;":[70],"large":[73],"cardinalities":[74],"were":[75,104],"selected":[76],"be":[78],"able":[79],"train":[81],"synthetic":[82],"variants":[83],"base":[85,120],"classifiers":[86,113,121],"different":[88,94],"subsets.To":[89],"check":[90],"models":[92],"numbers":[95],"possible":[97],"ensemble":[98],"members,":[99],"respective":[101],"pool":[102],"sizes":[103],"set":[105],"30":[107],"100;the":[109],"necessary":[110],"number":[111],"has":[114],"been":[115],"reached":[116],"via":[117],"synthesizing":[118],"with":[122],"training":[123,129],"them":[124],"differentsubsets":[126],"part":[130],"given":[133],"datasets.":[134]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
