{"id":"https://openalex.org/W3033637419","doi":"https://doi.org/10.1186/s40537-020-00312-x","title":"Investigating the relationship between time and predictive model maintenance","display_name":"Investigating the relationship between time and predictive model maintenance","publication_year":2020,"publication_date":"2020-06-05","ids":{"openalex":"https://openalex.org/W3033637419","doi":"https://doi.org/10.1186/s40537-020-00312-x","mag":"3033637419"},"language":"en","primary_location":{"id":"doi:10.1186/s40537-020-00312-x","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-020-00312-x","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-020-00312-x","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-020-00312-x","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004094853","display_name":"Joffrey L. Leevy","orcid":"https://orcid.org/0000-0002-7079-7540"},"institutions":[{"id":"https://openalex.org/I63772739","display_name":"Florida Atlantic University","ror":"https://ror.org/05p8w6387","country_code":"US","type":"education","lineage":["https://openalex.org/I63772739"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Joffrey L. Leevy","raw_affiliation_strings":["Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, USA"],"raw_orcid":"https://orcid.org/0000-0002-7079-7540","affiliations":[{"raw_affiliation_string":"Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, USA","institution_ids":["https://openalex.org/I63772739"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089170562","display_name":"Taghi M. Khoshgoftaar","orcid":null},"institutions":[{"id":"https://openalex.org/I63772739","display_name":"Florida Atlantic University","ror":"https://ror.org/05p8w6387","country_code":"US","type":"education","lineage":["https://openalex.org/I63772739"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Taghi M. Khoshgoftaar","raw_affiliation_strings":["Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, USA","institution_ids":["https://openalex.org/I63772739"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068409386","display_name":"Richard A. Bauder","orcid":null},"institutions":[{"id":"https://openalex.org/I63772739","display_name":"Florida Atlantic University","ror":"https://ror.org/05p8w6387","country_code":"US","type":"education","lineage":["https://openalex.org/I63772739"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Richard A. Bauder","raw_affiliation_strings":["Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, USA","institution_ids":["https://openalex.org/I63772739"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016269837","display_name":"Naeem Seliya","orcid":"https://orcid.org/0009-0001-1453-0227"},"institutions":[{"id":"https://openalex.org/I150722199","display_name":"Ohio Northern University","ror":"https://ror.org/052963a64","country_code":"US","type":"education","lineage":["https://openalex.org/I150722199","https://openalex.org/I4210162304"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Naeem Seliya","raw_affiliation_strings":["Ohio Northern University, 525 South Main Street, Ada, OH, 45810, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ohio Northern University, 525 South Main Street, Ada, OH, 45810, USA","institution_ids":["https://openalex.org/I150722199"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5004094853"],"corresponding_institution_ids":["https://openalex.org/I63772739"],"apc_list":{"value":1060,"currency":"GBP","value_usd":1300},"apc_paid":{"value":1060,"currency":"GBP","value_usd":1300},"fwci":2.582,"has_fulltext":true,"cited_by_count":23,"citation_normalized_percentile":{"value":0.91638529,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"7","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9998999834060669,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9998999834060669,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9958000183105469,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.993399977684021,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/undersampling","display_name":"Undersampling","score":0.8017243146896362},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7356215715408325},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.7109178304672241},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6468225717544556},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6035072207450867},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.597101628780365},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.572855532169342},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5219038128852844},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4959755837917328},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.4587138295173645},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38770371675491333},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3541385531425476},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19258207082748413}],"concepts":[{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.8017243146896362},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7356215715408325},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7109178304672241},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6468225717544556},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6035072207450867},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.597101628780365},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.572855532169342},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5219038128852844},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4959755837917328},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.4587138295173645},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38770371675491333},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3541385531425476},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19258207082748413},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s40537-020-00312-x","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-020-00312-x","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-020-00312-x","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:438e8c7312c244908f9c1b2c8ad702b0","is_oa":true,"landing_page_url":"https://doaj.org/article/438e8c7312c244908f9c1b2c8ad702b0","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Big Data, Vol 7, Iss 1, Pp 1-19 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s40537-020-00312-x","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-020-00312-x","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-020-00312-x","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7200000286102295,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G5416735113","display_name":"MRI: Acquisition of Big Data Training and Research Laboratory","funder_award_id":"1427536","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6293531692","display_name":null,"funder_award_id":"CNS-1427536","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320310801","display_name":"Florida Atlantic University","ror":"https://ror.org/05p8w6387"},{"id":"https://openalex.org/F4320317380","display_name":"Universidad del Atl\u00e1ntico","ror":"https://ror.org/05mm1w714"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3033637419.pdf","grobid_xml":"https://content.openalex.org/works/W3033637419.grobid-xml"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W139044672","https://openalex.org/W1497131193","https://openalex.org/W1570448133","https://openalex.org/W1851258275","https://openalex.org/W1941659294","https://openalex.org/W1964544799","https://openalex.org/W1975314866","https://openalex.org/W1977177161","https://openalex.org/W2010778044","https://openalex.org/W2022775778","https://openalex.org/W2023938061","https://openalex.org/W2026131661","https://openalex.org/W2028138594","https://openalex.org/W2044036264","https://openalex.org/W2048874599","https://openalex.org/W2058409108","https://openalex.org/W2058731966","https://openalex.org/W2058906820","https://openalex.org/W2089927030","https://openalex.org/W2095178814","https://openalex.org/W2099302642","https://openalex.org/W2099419573","https://openalex.org/W2101754595","https://openalex.org/W2106393550","https://openalex.org/W2118978333","https://openalex.org/W2119683921","https://openalex.org/W2121784101","https://openalex.org/W2125055259","https://openalex.org/W2140475657","https://openalex.org/W2153635508","https://openalex.org/W2157941646","https://openalex.org/W2161544295","https://openalex.org/W2171095014","https://openalex.org/W2289463038","https://openalex.org/W2318802957","https://openalex.org/W2564754306","https://openalex.org/W2567086669","https://openalex.org/W2585510674","https://openalex.org/W2887451702","https://openalex.org/W2891398562","https://openalex.org/W2892312135","https://openalex.org/W2899434936","https://openalex.org/W2912707296","https://openalex.org/W2919984788","https://openalex.org/W2975040691","https://openalex.org/W3008163412","https://openalex.org/W4229980202","https://openalex.org/W6630226126"],"related_works":["https://openalex.org/W2109073422","https://openalex.org/W2887783772","https://openalex.org/W2101754595","https://openalex.org/W4249381695","https://openalex.org/W3165582150","https://openalex.org/W4293261997","https://openalex.org/W2074765259","https://openalex.org/W3191198889","https://openalex.org/W2802491896","https://openalex.org/W4383180931"],"abstract_inverted_index":{"Abstract":[0],"A":[1],"majority":[2],"of":[3,26,41,91,111,125],"predictive":[4,56],"models":[5,149,245],"should":[6],"be":[7,34,215,241],"updated":[8],"regularly,":[9],"since":[10],"the":[11,17,27,39,42,51,105,112,116,123,139,142,151,160,173,186,196,205,211,222,247],"most":[12],"recent":[13],"data":[14,65,213],"associated":[15],"with":[16,159,172,185],"model":[18,57,224],"may":[19,33,214,239],"have":[20,77],"a":[21,164,177],"different":[22,226],"distribution":[23],"from":[24,66,89,104,115],"that":[25,76,195,210,220,235],"original":[28],"training":[29,87,198],"data.":[30,107],"This":[31],"difference":[32],"critical":[35],"enough":[36],"to":[37],"impact":[38],"effectiveness":[40],"machine":[43],"learning":[44],"model.":[45],"In":[46],"our":[47],"paper,":[48],"we":[49,218],"investigate":[50],"relationship":[52],"between":[53],"time":[54],"and":[55,74,97,133,182,231],"maintenance.":[58],"Our":[59,99,191],"work":[60],"incorporates":[61],"severely":[62],"imbalanced":[63],"big":[64],"three":[67,237],"Medicare":[68,248],"datasets,":[69,122],"namely":[70],"Part":[71,228],"D,":[72,229],"DMEPOS,":[73,230],"Combined,":[75,232],"been":[78],"used":[79],"in":[80,120],"several":[81],"fraud":[82,249],"detection":[83,250],"studies.":[84],"We":[85],"build":[86],"datasets":[88,101,238],"year-groupings":[90],"2013,":[92],"2014,":[93],"2015,":[94],"2013\u20132014,":[95],"2014\u20132015,":[96],"2013\u20132015.":[98],"test":[100],"are":[102,154],"built":[103],"2016":[106],"To":[108],"mitigate":[109],"some":[110],"adverse":[113],"effects":[114],"severe":[117],"class":[118,127,166,179],"imbalance":[119],"these":[121,236],"performance":[124],"five":[126,134],"ratios":[128],"obtained":[129],"by":[130,138],"Random":[131,170],"Undersampling":[132],"learners":[135],"is":[136,225],"evaluated":[137],"Area":[140],"Under":[141],"Receiver":[143],"Operating":[144],"Characteristic":[145],"Curve":[146],"metric.":[147],"The":[148],"producing":[150],"best":[152,223],"values":[153],"as":[155],"follows:":[156],"Logistic":[157,183],"Regression":[158,184],"2015":[161,188],"year-grouping":[162,175,189],"at":[163,176],"99:1":[165],"ratio":[167,180],"(Part":[168],"D);":[169],"Forest":[171],"2014-2015":[174],"75:25":[178],"(DMEPOS);":[181],"full":[187],"(Combined).":[190],"experimental":[192],"results":[193],"show":[194],"largest":[197],"dataset":[199],"(year-grouping":[200],"2013\u20132015)":[201],"was":[202],"not":[203],"among":[204],"selected":[206],"choices,":[207],"which":[208],"indicates":[209],"2013":[212],"outdated.":[216],"Moreover,":[217],"note":[219],"because":[221],"for":[227],"this":[233],"suggests":[234],"actually":[240],"sub-domains":[242],"requiring":[243],"unique":[244],"within":[246],"domain.":[251]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}