{"id":"https://openalex.org/W2985917055","doi":"https://doi.org/10.1109/access.2019.2951189","title":"Training Back Propagation Neural Networks in MapReduce on High-Dimensional Big Datasets With Global Evolution","display_name":"Training Back Propagation Neural Networks in MapReduce on High-Dimensional Big Datasets With Global Evolution","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2985917055","doi":"https://doi.org/10.1109/access.2019.2951189","mag":"2985917055"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2951189","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08890639.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_indexed_in_scopus":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},"type":"article","type_crossref":"journal-article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08890639.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046558187","display_name":"Wanghu Chen","orcid":"https://orcid.org/0000-0002-9233-7609"},"institutions":[{"id":"https://openalex.org/I68986083","display_name":"Northwest Normal University","ror":"https://ror.org/00gx3j908","country_code":"CN","type":"funder","lineage":["https://openalex.org/I68986083"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanghu Chen","raw_affiliation_strings":["College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China","institution_ids":["https://openalex.org/I68986083"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100717555","display_name":"Jing Li","orcid":"https://orcid.org/0000-0002-3773-701X"},"institutions":[{"id":"https://openalex.org/I68986083","display_name":"Northwest Normal University","ror":"https://ror.org/00gx3j908","country_code":"CN","type":"funder","lineage":["https://openalex.org/I68986083"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Li","raw_affiliation_strings":["College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China","institution_ids":["https://openalex.org/I68986083"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101701703","display_name":"Xintian Li","orcid":"https://orcid.org/0000-0002-5698-1288"},"institutions":[{"id":"https://openalex.org/I68986083","display_name":"Northwest Normal University","ror":"https://ror.org/00gx3j908","country_code":"CN","type":"funder","lineage":["https://openalex.org/I68986083"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xintian Li","raw_affiliation_strings":["College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China","institution_ids":["https://openalex.org/I68986083"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100663892","display_name":"Lizhi Zhang","orcid":"https://orcid.org/0000-0001-6727-1962"},"institutions":[{"id":"https://openalex.org/I68986083","display_name":"Northwest Normal University","ror":"https://ror.org/00gx3j908","country_code":"CN","type":"funder","lineage":["https://openalex.org/I68986083"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lizhi Zhang","raw_affiliation_strings":["College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China","institution_ids":["https://openalex.org/I68986083"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101750217","display_name":"Jianwu Wang","orcid":"https://orcid.org/0000-0002-9933-1170"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"funder","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianwu Wang","raw_affiliation_strings":["Department of Information Systems, University of Maryland, Baltimore County, Baltimore, USA"],"affiliations":[{"raw_affiliation_string":"Department of Information Systems, University of Maryland, Baltimore County, Baltimore, USA","institution_ids":["https://openalex.org/I79272384"]}]}],"institution_assertions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":true,"fulltext_origin":"pdf","cited_by_count":1,"citation_normalized_percentile":{"value":0.463859,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":61,"max":69},"biblio":{"volume":"7","issue":null,"first_page":"159855","last_page":"159867"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9999,"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/T12676","display_name":"Machine Learning and ELM","score":0.9999,"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/T10320","display_name":"Neural Networks and Applications","score":0.9998,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9994,"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/deep-neural-networks","display_name":"Deep Neural Networks","score":0.4486447},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.42831165}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8603761},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.8073291},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.687638},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.536399},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5178171},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5113113},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.50148845},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48962075},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.48168382},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4486447},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.42831165},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4208411},{"id":"https://openalex.org/C63540848","wikidata":"https://www.wikidata.org/wiki/Q3140932","display_name":"Fault tolerance","level":2,"score":0.41746283},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36961323},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.36728072},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.15640026},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.09544465},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2951189","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08890639.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_indexed_in_scopus":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},{"is_oa":false,"landing_page_url":"https://doaj.org/article/2b7fb43a91694c71a53e28ba9ed2b017","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"http://hdl.handle.net/11603/31606","pdf_url":"https://mdsoar.org/bitstreams/653491ac-017a-44b2-8608-9942de47b2dd/download","source":{"id":"https://openalex.org/S4306402556","display_name":"Maryland Shared Open Access Repository (USMAI Consortium)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":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":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2951189","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08890639.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_indexed_in_scopus":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.46}],"grants":[{"funder":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China","award_id":"61967013"},{"funder":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China","award_id":"61462076"}],"datasets":[],"versions":[],"referenced_works_count":46,"referenced_works":["https://openalex.org/W1499701685","https://openalex.org/W1594505553","https://openalex.org/W1967573487","https://openalex.org/W1975744095","https://openalex.org/W1988115241","https://openalex.org/W2003657827","https://openalex.org/W2003756933","https://openalex.org/W2004436696","https://openalex.org/W2016055711","https://openalex.org/W2032861771","https://openalex.org/W2055808629","https://openalex.org/W2061765694","https://openalex.org/W2081492751","https://openalex.org/W2082703217","https://openalex.org/W2088743561","https://openalex.org/W2090898720","https://openalex.org/W2095705004","https://openalex.org/W2101927907","https://openalex.org/W2115207808","https://openalex.org/W2120138324","https://openalex.org/W2122178759","https://openalex.org/W2124290836","https://openalex.org/W2145431022","https://openalex.org/W2157649680","https://openalex.org/W2160603000","https://openalex.org/W2279613615","https://openalex.org/W2291242721","https://openalex.org/W2294917463","https://openalex.org/W2395318016","https://openalex.org/W2467826469","https://openalex.org/W2474603704","https://openalex.org/W2519754487","https://openalex.org/W2530879419","https://openalex.org/W2555012488","https://openalex.org/W2555730531","https://openalex.org/W2558611266","https://openalex.org/W2562601370","https://openalex.org/W2750793847","https://openalex.org/W2766736793","https://openalex.org/W2783017375","https://openalex.org/W2905413452","https://openalex.org/W2944481182","https://openalex.org/W2944781153","https://openalex.org/W2960146582","https://openalex.org/W4236546113","https://openalex.org/W4246050513"],"related_works":["https://openalex.org/W589102260","https://openalex.org/W4405901645","https://openalex.org/W4394895745","https://openalex.org/W4390608645","https://openalex.org/W4247566972","https://openalex.org/W3090563135","https://openalex.org/W2960264696","https://openalex.org/W2497432351","https://openalex.org/W2088845016","https://openalex.org/W1966421350"],"abstract_inverted_index":{"Owing":[0],"to":[1,21,57,72,96,111,166,175,196],"its":[2],"scalability":[3],"and":[4,204],"high":[5],"fault-tolerance":[6],"even":[7],"on":[8,31,36,83,118,138],"a":[9,54,69,115],"distributed":[10,44,59],"environment":[11],"built":[12],"up":[13],"with":[14,47,213],"personal":[15],"computers,":[16],"MapReduce":[17,201],"has":[18,225],"been":[19],"introduced":[20],"parallelise":[22],"the":[23,37,51,58,84,94,98,109,124,131,139,151,154,168,177,181,191,197],"training":[24,61,110,125,192],"of":[25,39,62,141,150,170],"Back":[26],"Propagation":[27],"Neural":[28],"Networks":[29],"(BPNNs)":[30],"high-dimensional":[32,119],"big":[33,120,226],"datasets.":[34,121,210],"Based":[35],"evolution":[38,182],"local":[40,79,116,128],"BPNNs":[41,63,80,129],"produced":[42],"by":[43],"Map":[45],"tasks":[46],"different":[48],"data":[49,86],"splits,":[50],"paper":[52],"proposes":[53],"novel":[55],"approach":[56,67,158,189,224],"data-parallel":[60],"in":[64,107,173,180,228],"MapReduce.":[65],"The":[66,211],"provides":[68],"reasonable":[70],"measure":[71],"get":[73,97,112],"global":[74,99,155],"convergent":[75,82,100],"BPNN":[76,202],"candidates":[77],"from":[78,130],"only":[81,91],"specific":[85],"splits.":[87],"Further,":[88],"it":[89],"not":[90],"can":[92],"reduce":[93],"iterations":[95],"BPNN,":[101],"but":[102],"also":[103,159,219],"shows":[104,220],"great":[105],"advantages":[106,227],"avoiding":[108],"trapped":[113],"into":[114],"optimum":[117],"To":[122],"improve":[123],"efficiency":[126,193],"further,":[127],"same":[132],"computing":[133],"node":[134],"are":[135],"merged":[136],"based":[137,163],"average":[140],"their":[142],"weight":[143],"matrices":[144],"before":[145],"they":[146],"act":[147],"as":[148],"individuals":[149],"population":[152],"for":[153,208],"evolution.":[156],"Our":[157],"leverages":[160],"Random":[161],"Project":[162],"sampling":[164],"techniques":[165],"evaluate":[167],"fitness":[169],"each":[171],"individual":[172],"order":[174],"lower":[176],"computation":[178],"cost":[179],"stage.":[183],"Experiments":[184],"show":[185],"that":[186,221],"our":[187,222],"proposed":[188,223],"improves":[190,205],"highly":[194],"compared":[195],"stand-alone":[198],"or":[199],"traditional":[200],"training,":[203],"model":[206],"accuracy":[207],"larger":[209],"comparison":[212],"23":[214],"other":[215],"popular":[216],"classification":[217],"approaches":[218],"accuracy.":[229]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2985917055","counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-04-15T20:21:24.522142","created_date":"2019-11-22"}