{"id":"https://openalex.org/W2759501326","doi":"https://doi.org/10.1109/bigdata.2017.8258364","title":"Introducing DeepBalance: Random deep belief network ensembles to address class imbalance","display_name":"Introducing DeepBalance: Random deep belief network ensembles to address class imbalance","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2759501326","doi":"https://doi.org/10.1109/bigdata.2017.8258364","mag":"2759501326"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2017.8258364","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258364","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1709.10056","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036139759","display_name":"Peter Xenopoulos","orcid":"https://orcid.org/0000-0001-8594-3400"},"institutions":[{"id":"https://openalex.org/I177881444","display_name":"Pomona College","ror":"https://ror.org/0074grg94","country_code":"US","type":"education","lineage":["https://openalex.org/I177881444"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Peter Xenopoulos","raw_affiliation_strings":["The mathematics and economics departments, Pomona College in Claremont, CA","Mathematics and economics departments at Pomona College in Claremont, CA 91711"],"affiliations":[{"raw_affiliation_string":"The mathematics and economics departments, Pomona College in Claremont, CA","institution_ids":["https://openalex.org/I177881444"]},{"raw_affiliation_string":"Mathematics and economics departments at Pomona College in Claremont, CA 91711","institution_ids":["https://openalex.org/I177881444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5036139759"],"corresponding_institution_ids":["https://openalex.org/I177881444"],"apc_list":null,"apc_paid":null,"fwci":0.4154,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.727257,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"3684","last_page":"3689"},"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/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13429","display_name":"Electricity Theft Detection Techniques","score":0.9498999714851379,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7512365579605103},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7270591259002686},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.705187201499939},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.592517614364624},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.48105528950691223},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4722822606563568},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37903064489364624}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7512365579605103},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7270591259002686},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.705187201499939},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.592517614364624},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.48105528950691223},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4722822606563568},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37903064489364624}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/bigdata.2017.8258364","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258364","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1709.10056","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1709.10056","pdf_url":"https://arxiv.org/pdf/1709.10056","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:2759501326","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1709.10056.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1709.10056","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1709.10056","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1709.10056","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1709.10056","pdf_url":"https://arxiv.org/pdf/1709.10056","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.8100000023841858}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2759501326.pdf","grobid_xml":"https://content.openalex.org/works/W2759501326.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W169052826","https://openalex.org/W1551909886","https://openalex.org/W1563938718","https://openalex.org/W1574715085","https://openalex.org/W1580791986","https://openalex.org/W1588282782","https://openalex.org/W1605695115","https://openalex.org/W1766594731","https://openalex.org/W1941659294","https://openalex.org/W1964812476","https://openalex.org/W1976526581","https://openalex.org/W1993220166","https://openalex.org/W1999318832","https://openalex.org/W2007272376","https://openalex.org/W2067594023","https://openalex.org/W2099454382","https://openalex.org/W2100495367","https://openalex.org/W2104167780","https://openalex.org/W2118978333","https://openalex.org/W2119168155","https://openalex.org/W2119191234","https://openalex.org/W2120457925","https://openalex.org/W2132791018","https://openalex.org/W2136903812","https://openalex.org/W2136922672","https://openalex.org/W2148143831","https://openalex.org/W2149308034","https://openalex.org/W2156876426","https://openalex.org/W2217007515","https://openalex.org/W2247512861","https://openalex.org/W2253923269","https://openalex.org/W2536062280","https://openalex.org/W2785726556","https://openalex.org/W4252441533","https://openalex.org/W6606879723","https://openalex.org/W6634333142","https://openalex.org/W6680202767","https://openalex.org/W6683036876","https://openalex.org/W6683161558","https://openalex.org/W7053126446"],"related_works":["https://openalex.org/W144837275","https://openalex.org/W2921283782","https://openalex.org/W2399563792","https://openalex.org/W2604243156","https://openalex.org/W478671","https://openalex.org/W2907465059","https://openalex.org/W3147779970","https://openalex.org/W2760693722","https://openalex.org/W2133928392","https://openalex.org/W3213788487","https://openalex.org/W3035999516","https://openalex.org/W1993220166","https://openalex.org/W2920156589","https://openalex.org/W2070789886","https://openalex.org/W2103768134","https://openalex.org/W3196630108","https://openalex.org/W2334028018","https://openalex.org/W2147594372","https://openalex.org/W2740195871","https://openalex.org/W3032632478"],"abstract_inverted_index":{"Class":[0],"imbalance":[1,59,76],"problems":[2],"manifest":[3],"in":[4,31,54,120,169],"domains":[5],"such":[6,113,122],"as":[7,40,60,67,114,123],"financial":[8,132],"fraud":[9],"detection":[10],"or":[11,80],"network":[12],"intrusion":[13],"analysis,":[14],"where":[15],"the":[16,33,37,41,55,68],"prevalence":[17],"of":[18,57,91,144,167],"one":[19],"class":[20,35,43,58,75],"is":[21,154],"much":[22],"higher":[23,47],"than":[24,36],"another.":[25],"Typically,":[26],"practitioners":[27],"are":[28],"more":[29],"interested":[30],"predicting":[32],"minority":[34,42],"majority":[38,69],"class,":[39],"may":[44,63],"carry":[45],"a":[46],"misclassification":[48],"cost.":[49],"However,":[50],"classifier":[51],"performance":[52,139],"deteriorates":[53],"face":[56],"oftentimes":[61],"classifiers":[62],"predict":[64],"every":[65],"point":[66],"class.":[70],"Methods":[71],"for":[72],"dealing":[73],"with":[74,96],"include":[77],"cost-sensitive":[78],"learning":[79],"resampling":[81,111],"techniques.":[82],"In":[83],"this":[84],"paper,":[85],"we":[86,137,149,163],"introduce":[87],"DeepBalance,":[88],"an":[89,165],"ensemble":[90],"deep":[92],"belief":[93],"networks":[94],"trained":[95],"balanced":[97],"bootstraps":[98],"and":[99,116,118,125,140],"random":[100],"feature":[101],"selection.":[102],"We":[103],"demonstrate":[104],"that":[105,151],"our":[106,152],"proposed":[107],"method":[108],"outperforms":[109],"baseline":[110],"methods":[112],"SMOTE":[115],"under-":[117],"over-sampling":[119],"metrics":[121],"AUC":[124],"sensitivity":[126],"when":[127],"applied":[128],"to":[129],"highly":[130],"imbalanced":[131],"transaction":[133],"data":[134],"sets.":[135],"Additionally,":[136],"explore":[138],"training":[141,160],"time":[142],"implications":[143],"various":[145],"model":[146,153],"parameters.":[147],"Furthermore,":[148],"show":[150],"easily":[155],"parallelizable,":[156],"which":[157],"can":[158],"reduce":[159],"times.":[161],"Finally,":[162],"present":[164],"implementation":[166],"DeepBalance":[168],"R.":[170]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
