{"id":"https://openalex.org/W2605014652","doi":"https://doi.org/10.1145/3071178.3071215","title":"Ensemble representation learning","display_name":"Ensemble representation learning","publication_year":2017,"publication_date":"2017-06-30","ids":{"openalex":"https://openalex.org/W2605014652","doi":"https://doi.org/10.1145/3071178.3071215","mag":"2605014652"},"language":"en","primary_location":{"id":"doi:10.1145/3071178.3071215","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3071178.3071215","pdf_url":null,"source":{"id":"https://openalex.org/S4363608932","display_name":"Proceedings of the Genetic and Evolutionary Computation Conference","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Genetic and Evolutionary Computation Conference","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/A5002465837","display_name":"William La Cava","orcid":"https://orcid.org/0000-0002-1332-2960"},"institutions":[{"id":"https://openalex.org/I36788626","display_name":"California University of Pennsylvania","ror":"https://ror.org/01spssf70","country_code":"US","type":"education","lineage":["https://openalex.org/I36788626"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"William La Cava","raw_affiliation_strings":["University of Pennsylvania"],"affiliations":[{"raw_affiliation_string":"University of Pennsylvania","institution_ids":["https://openalex.org/I36788626"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032971510","display_name":"Jason H. Moore","orcid":"https://orcid.org/0000-0002-5015-1099"},"institutions":[{"id":"https://openalex.org/I36788626","display_name":"California University of Pennsylvania","ror":"https://ror.org/01spssf70","country_code":"US","type":"education","lineage":["https://openalex.org/I36788626"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jason H. Moore","raw_affiliation_strings":["University of Pennsylvania"],"affiliations":[{"raw_affiliation_string":"University of Pennsylvania","institution_ids":["https://openalex.org/I36788626"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5002465837"],"corresponding_institution_ids":["https://openalex.org/I36788626"],"apc_list":null,"apc_paid":null,"fwci":0.5192,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.72080391,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"961","last_page":"968"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9991000294685364,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9991000294685364,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9930999875068665,"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/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7130215764045715},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7037521004676819},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6950779557228088},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5815908908843994},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5583726167678833},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5234220027923584},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38711774349212646}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7130215764045715},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7037521004676819},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6950779557228088},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5815908908843994},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5583726167678833},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5234220027923584},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38711774349212646},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3071178.3071215","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3071178.3071215","pdf_url":null,"source":{"id":"https://openalex.org/S4363608932","display_name":"Proceedings of the Genetic and Evolutionary Computation Conference","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Genetic and Evolutionary Computation Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3380273709","display_name":null,"funder_award_id":"P30-ES013508","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W121066256","https://openalex.org/W1480376833","https://openalex.org/W1579738444","https://openalex.org/W1585335425","https://openalex.org/W1729367118","https://openalex.org/W1987971958","https://openalex.org/W1991542861","https://openalex.org/W1992338826","https://openalex.org/W1999927946","https://openalex.org/W2006534190","https://openalex.org/W2030861990","https://openalex.org/W2034068348","https://openalex.org/W2059396387","https://openalex.org/W2067650768","https://openalex.org/W2072473332","https://openalex.org/W2097932601","https://openalex.org/W2101234009","https://openalex.org/W2110905164","https://openalex.org/W2126964466","https://openalex.org/W2129018774","https://openalex.org/W2135046866","https://openalex.org/W2138550913","https://openalex.org/W2163922914","https://openalex.org/W2484166844","https://openalex.org/W2562150744","https://openalex.org/W2601848305","https://openalex.org/W2605078685","https://openalex.org/W2911964244","https://openalex.org/W2914206343","https://openalex.org/W3098382816","https://openalex.org/W6675354045"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4376643315","https://openalex.org/W4324137541","https://openalex.org/W2900445707","https://openalex.org/W4285741730","https://openalex.org/W1191482210","https://openalex.org/W4285046548","https://openalex.org/W4210302090","https://openalex.org/W3092276832","https://openalex.org/W4375951447"],"abstract_inverted_index":{"Recently":[0],"we":[1,25],"proposed":[2],"a":[3,14,33,95,103,130],"general,":[4],"ensemble-based":[5],"feature":[6],"engineering":[7],"wrapper":[8],"(FEW)":[9],"that":[10,47,73,110,123],"was":[11],"paired":[12],"with":[13,133],"number":[15],"of":[16,36,56,106],"machine":[17],"learning":[18],"methods":[19,40,70,101],"to":[20,98],"solve":[21],"regression":[22],"problems.":[23],"Here,":[24],"adapt":[26],"FEW":[27,111,124],"for":[28,129],"supervised":[29],"classification":[30,100],"and":[31,38,71,90,108],"perform":[32],"thorough":[34],"analysis":[35],"fitness":[37,49],"survival":[39,69,75,85],"within":[41],"this":[42],"framework.":[43],"Our":[44],"tests":[45],"demonstrate":[46,72],"two":[48],"metrics,":[50],"one":[51],"introduced":[52],"as":[53],"an":[54],"adaptation":[55],"the":[57,61,114],"silhouette":[58],"score,":[59],"outperform":[60],"more":[62],"commonly":[63],"used":[64],"Fisher":[65],"criterion.":[66],"We":[67,93,121],"analyze":[68],"\u03f5-lexicase":[74],"works":[76],"best":[77,115],"across":[78],"our":[79],"test":[80],"problems,":[81],"followed":[82],"by":[83],"random":[84],"which":[86],"outperforms":[87],"both":[88],"tournament":[89],"deterministic":[91],"crowding.":[92],"conduct":[94],"benchmark":[96],"comparison":[97],"several":[99,119],"using":[102],"large":[104],"set":[105],"problems":[107],"show":[109,122],"can":[112],"improve":[113],"classifier":[116],"performance":[117],"in":[118],"cases.":[120],"generates":[125],"consistent,":[126],"meaningful":[127],"features":[128],"biomedical":[131],"problem":[132],"different":[134],"ML":[135],"pairings.":[136]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
