{"id":"https://openalex.org/W1974003465","doi":"https://doi.org/10.1109/ijcnn.2007.4371106","title":"Ensemble Learning Based on the Output Sensitivity of Multilayer Perceptrons","display_name":"Ensemble Learning Based on the Output Sensitivity of Multilayer Perceptrons","publication_year":2007,"publication_date":"2007-08-01","ids":{"openalex":"https://openalex.org/W1974003465","doi":"https://doi.org/10.1109/ijcnn.2007.4371106","mag":"1974003465"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2007.4371106","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2007.4371106","pdf_url":null,"source":{"id":"https://openalex.org/S4210195743","display_name":"IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks","issn_l":"1098-7576","issn":["1098-7576","1558-3902"],"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 International Joint Conference on Neural Networks","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/A5100861156","display_name":"Jia Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jia Tang","raw_affiliation_strings":["Department of Computer Science and Engineering, Hohai University, HHU, Nanjing, China","Hohai university Nanjing"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Hohai University, HHU, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]},{"raw_affiliation_string":"Hohai university Nanjing","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102017246","display_name":"Xiaoqin Zeng","orcid":"https://orcid.org/0000-0002-4687-901X"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoqin Zeng","raw_affiliation_strings":["Department of Computer Science and Engineering, Hohai University, HHU, Nanjing, China","Hohai university Nanjing"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Hohai University, HHU, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]},{"raw_affiliation_string":"Hohai university Nanjing","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053733933","display_name":"Lei L\u00fc","orcid":"https://orcid.org/0000-0001-9139-8955"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Lu","raw_affiliation_strings":["Department of Computer Science and Engineering, Hohai University, HHU, Nanjing, China","Hohai university Nanjing"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Hohai University, HHU, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]},{"raw_affiliation_string":"Hohai university Nanjing","institution_ids":["https://openalex.org/I163340411"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100861156"],"corresponding_institution_ids":["https://openalex.org/I163340411"],"apc_list":null,"apc_paid":null,"fwci":0.7611,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.68317503,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"7","issue":null,"first_page":"1067","last_page":"1072"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9993000030517578,"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":0.9993000030517578,"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/T10057","display_name":"Face and Expression Recognition","score":0.9972000122070312,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9789999723434448,"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/boosting","display_name":"Boosting (machine learning)","score":0.7993654012680054},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7580682039260864},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.7403901815414429},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6562061309814453},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6341841220855713},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.6036697030067444},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6013555526733398},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.5691860318183899},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5443231463432312},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5022447109222412},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.4393244683742523},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4241902232170105},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.41569340229034424},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34569889307022095},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1629411280155182}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7993654012680054},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7580682039260864},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.7403901815414429},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6562061309814453},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6341841220855713},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.6036697030067444},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6013555526733398},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.5691860318183899},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5443231463432312},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5022447109222412},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.4393244683742523},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4241902232170105},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.41569340229034424},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34569889307022095},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1629411280155182},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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/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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2007.4371106","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2007.4371106","pdf_url":null,"source":{"id":"https://openalex.org/S4210195743","display_name":"IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks","issn_l":"1098-7576","issn":["1098-7576","1558-3902"],"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 International Joint Conference on Neural Networks","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W167329903","https://openalex.org/W1540371141","https://openalex.org/W2019778169","https://openalex.org/W2100128988","https://openalex.org/W2111221339","https://openalex.org/W2117783555","https://openalex.org/W2119113516","https://openalex.org/W2128073546","https://openalex.org/W2135293965","https://openalex.org/W2158875652","https://openalex.org/W2167055186","https://openalex.org/W2331432542","https://openalex.org/W2532140063","https://openalex.org/W2912934387","https://openalex.org/W3106889297","https://openalex.org/W4212883601","https://openalex.org/W6606757156","https://openalex.org/W6632223008","https://openalex.org/W6677494230","https://openalex.org/W6679154154"],"related_works":["https://openalex.org/W4239608116","https://openalex.org/W2905156999","https://openalex.org/W4229460275","https://openalex.org/W2002382339","https://openalex.org/W4296079469","https://openalex.org/W1987518466","https://openalex.org/W3135046080","https://openalex.org/W3023033471","https://openalex.org/W2014852328","https://openalex.org/W2099182244"],"abstract_inverted_index":{"Ensemble":[0],"learning":[1],"to":[2,15,18,57,115,148],"construct":[3,58],"learners":[4],"in":[5,123,212],"regression":[6],"and":[7,11,37,45,135,188,198,215],"classification":[8],"has":[9,204],"practically":[10],"theoretically":[12],"been":[13,186],"proved":[14],"be":[16,113],"able":[17],"improve":[19],"the":[20,24,77,88,102,105,110,118,124,136,141,156,164,167,181,189,208],"generalization":[21,216],"capability":[22],"of":[23,70,73,84,104,144,166,175,191],"learners.":[25],"Nowadays,":[26],"most":[27,78,168],"neural":[28,59],"network":[29,60],"ensembles":[30,61],"are":[31,132],"obtained":[32],"by":[33,68],"manipulating":[34],"training":[35],"data":[36,98,152,184],"networks'":[38],"architecture":[39],"etc,":[40],"such":[41],"as":[42,140],"Bagging,":[43],"Boosting,":[44],"other":[46],"methods":[47,211],"like":[48],"evolutionary":[49],"techniques.":[50],"In":[51,126],"this":[52],"paper,":[53],"a":[54,82,91,96,172],"new":[55],"method":[56,203],"is":[62,138],"presented,":[63],"which":[64],"aims":[65],"at":[66,95,151],"selecting,":[67],"means":[69],"output":[71,93,107,119,147],"sensitivity":[72,89,111,137],"an":[74,145],"individual":[75],"network,":[76],"diverse":[79,169],"members":[80],"from":[81,171,196],"pool":[83,174],"trained":[85,176],"networks.":[86],"Conceptually,":[87],"reflects":[90],"network's":[92,106],"behavior":[94],"given":[97,173],"point,":[99],"for":[100,163],"example,":[101],"trend":[103],"nearby.":[108],"So":[109],"can":[112],"helpful":[114],"explicitly":[116],"measure":[117],"diversity":[120],"among":[121],"individuals":[122,170],"pool.":[125],"our":[127,192,202],"research,":[128],"Multilayer":[129],"Perceptrons":[130],"(MLPs)":[131],"focused":[133],"on,":[134],"adopted":[139],"partial":[142],"derivative":[143],"MLP's":[146],"its":[149],"input":[150],"point.":[153],"Based":[154],"on":[155,180],"sensitivity,":[157],"we":[158],"developed":[159],"four":[160],"different":[161],"measures":[162],"selection":[165],"MLPs.":[177],"Some":[178],"experiments":[179],"UCI":[182],"benchmark":[183],"have":[185],"conducted,":[187],"comparisons":[190],"results":[193],"with":[194],"those":[195],"Bagging":[197],"Boosting":[199],"show":[200],"that":[201],"some":[205],"advantages":[206],"over":[207],"existing":[209],"ensemble":[210,213],"size":[214],"performance.":[217]},"counts_by_year":[{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}