{"id":"https://openalex.org/W4200559451","doi":"https://doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom52081.2021.00086","title":"An Effective Outlier Detection Method for EAF Based on an Iterative Heterogeneous Ensemble","display_name":"An Effective Outlier Detection Method for EAF Based on an Iterative Heterogeneous Ensemble","publication_year":2021,"publication_date":"2021-09-01","ids":{"openalex":"https://openalex.org/W4200559451","doi":"https://doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom52081.2021.00086"},"language":"en","primary_location":{"id":"doi:10.1109/ispa-bdcloud-socialcom-sustaincom52081.2021.00086","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom52081.2021.00086","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Intl Conf on Parallel &amp; Distributed Processing with Applications, Big Data &amp; Cloud Computing, Sustainable Computing &amp; Communications, Social Computing &amp; Networking (ISPA/BDCloud/SocialCom/SustainCom)","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/A5107250419","display_name":"Jiong Zhang","orcid":"https://orcid.org/0000-0002-4582-9299"},"institutions":[{"id":"https://openalex.org/I4210154121","display_name":"Shandong Institute of Commerce & Technology","ror":"https://ror.org/03xk2yz39","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210154121"]},{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiong Zhang","raw_affiliation_strings":["School of Information, Shandong Institute of Commerce and Technology, Jinan, China","School of Software, Shandong University, Jinan, China"],"affiliations":[{"raw_affiliation_string":"School of Information, Shandong Institute of Commerce and Technology, Jinan, China","institution_ids":["https://openalex.org/I4210154121"]},{"raw_affiliation_string":"School of Software, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100439483","display_name":"Biao Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Biao Wang","raw_affiliation_strings":["Schoole of Automation, Shenyang Aerospace University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"Schoole of Automation, Shenyang Aerospace University, Shenyang, China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052295974","display_name":"Hongjun Dai","orcid":"https://orcid.org/0000-0002-1075-8750"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongjun Dai","raw_affiliation_strings":["School of Software, Shandong University, Jinan, China"],"affiliations":[{"raw_affiliation_string":"School of Software, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5107250419"],"corresponding_institution_ids":["https://openalex.org/I154099455","https://openalex.org/I4210154121"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19049014,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"72","issue":null,"first_page":"588","last_page":"593"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12282","display_name":"Mineral Processing and Grinding","score":0.9606000185012817,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T12282","display_name":"Mineral Processing and Grinding","score":0.9606000185012817,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9416000247001648,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T14347","display_name":"Big Data and Digital Economy","score":0.9118000268936157,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/outlier","display_name":"Outlier","score":0.8891686201095581},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7798198461532593},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6714953780174255},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6216611266136169},{"id":"https://openalex.org/keywords/iterative-and-incremental-development","display_name":"Iterative and incremental development","score":0.6035964488983154},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5220324993133545},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5177761316299438},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.4861021041870117},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.4762268662452698},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.47399723529815674},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.453841894865036},{"id":"https://openalex.org/keywords/local-outlier-factor","display_name":"Local outlier factor","score":0.45133548974990845},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44454851746559143},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44394952058792114},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3340336084365845},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1804634928703308}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.8891686201095581},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7798198461532593},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6714953780174255},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6216611266136169},{"id":"https://openalex.org/C143587482","wikidata":"https://www.wikidata.org/wiki/Q1543216","display_name":"Iterative and incremental development","level":2,"score":0.6035964488983154},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5220324993133545},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5177761316299438},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.4861021041870117},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4762268662452698},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.47399723529815674},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.453841894865036},{"id":"https://openalex.org/C169029474","wikidata":"https://www.wikidata.org/wiki/Q387942","display_name":"Local outlier factor","level":3,"score":0.45133548974990845},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44454851746559143},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44394952058792114},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3340336084365845},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1804634928703308},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","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/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"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/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ispa-bdcloud-socialcom-sustaincom52081.2021.00086","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom52081.2021.00086","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Intl Conf on Parallel &amp; Distributed Processing with Applications, Big Data &amp; Cloud Computing, Sustainable Computing &amp; Communications, Social Computing &amp; Networking (ISPA/BDCloud/SocialCom/SustainCom)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320324174","display_name":"Natural Science Foundation of Shandong Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W42722137","https://openalex.org/W164607750","https://openalex.org/W761890246","https://openalex.org/W1488833649","https://openalex.org/W1560461363","https://openalex.org/W1853854734","https://openalex.org/W1965833531","https://openalex.org/W2001014415","https://openalex.org/W2001181054","https://openalex.org/W2009035836","https://openalex.org/W2011070474","https://openalex.org/W2036594035","https://openalex.org/W2048590178","https://openalex.org/W2061783148","https://openalex.org/W2083059234","https://openalex.org/W2083911205","https://openalex.org/W2088706790","https://openalex.org/W2094247334","https://openalex.org/W2097714558","https://openalex.org/W2098487666","https://openalex.org/W2100294832","https://openalex.org/W2101549186","https://openalex.org/W2122646361","https://openalex.org/W2123537198","https://openalex.org/W2126322661","https://openalex.org/W2136812936","https://openalex.org/W2153581658","https://openalex.org/W2160868604","https://openalex.org/W2161234287","https://openalex.org/W2175561235","https://openalex.org/W2177464891","https://openalex.org/W2181002552","https://openalex.org/W2249372831","https://openalex.org/W2469843020","https://openalex.org/W2511107577","https://openalex.org/W2571664857","https://openalex.org/W2759003256","https://openalex.org/W2768789311","https://openalex.org/W2800644345","https://openalex.org/W2888908791","https://openalex.org/W2904822718","https://openalex.org/W2972287909","https://openalex.org/W3151829154","https://openalex.org/W4212883601","https://openalex.org/W4254420769","https://openalex.org/W6629355409","https://openalex.org/W6682523880"],"related_works":["https://openalex.org/W2770832849","https://openalex.org/W205872183","https://openalex.org/W114119537","https://openalex.org/W2904893831","https://openalex.org/W2912112202","https://openalex.org/W3117098906","https://openalex.org/W4304761972","https://openalex.org/W2889390244","https://openalex.org/W3197833032","https://openalex.org/W2787338698"],"abstract_inverted_index":{"With":[0],"the":[1,27,44,71,110,121,130],"increasing":[2],"applications":[3],"of":[4,29,31,46,58,73,84,123],"data-driven":[5],"techniques":[6],"in":[7,24,82,101,156],"Electric":[8],"Arc":[9],"Furnace":[10],"(EAF)":[11],"steelmaking,":[12],"outlier":[13,51,60,80,90,99,132],"detection":[14,48,148],"has":[15,125],"drawn":[16],"more":[17],"attention":[18],"than":[19],"before.":[20],"By":[21,108],"removing":[22],"outliers":[23,124],"EAF":[25,135],"datasets,":[26],"performance":[28],"methods":[30],"process":[32,34],"monitoring,":[33],"control,":[35],"and":[36,64,153],"system":[37],"modeling":[38],"can":[39,77],"be":[40],"improved.":[41],"To":[42,92],"overcome":[43],"drawback":[45],"single":[47,154],"technique,":[49],"several":[50],"ensembles":[52,61,81,152],"have":[53,144],"been":[54,126],"proposed.":[55],"However,":[56],"structures":[57],"these":[59],"are":[62,106],"fixed":[63],"only":[65],"variance":[66],"reduction":[67,86],"is":[68],"considered.":[69],"From":[70],"perspective":[72],"bias-variance":[74],"tradeoff,":[75],"we":[76,95,114],"also":[78],"construct":[79],"terms":[83],"bias":[85],"to":[87,134,137],"obtain":[88,116],"stronger":[89],"ensembles.":[91],"this":[93],"end,":[94],"propose":[96],"an":[97,117],"iterative":[98],"ensemble,":[100],"which":[102],"heterogeneous":[103],"base":[104],"learners":[105],"used.":[107],"pruning":[109],"training":[111],"set":[112],"iteratively,":[113],"will":[115],"effective":[118],"ensemble":[119,133],"since":[120],"influence":[122],"alleviated.":[127],"We":[128],"apply":[129],"proposed":[131],"datasets":[136],"verify":[138],"its":[139],"performance.":[140],"The":[141],"experimental":[142],"results":[143],"shown":[145],"that":[146],"our":[147],"method":[149],"outperforms":[150],"other":[151],"models":[155],"most":[157],"cases.":[158]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
