{"id":"https://openalex.org/W2220584357","doi":"https://doi.org/10.1142/s1469026815500224","title":"A Cluster-Based Data Balancing Ensemble Classifier for Response Modeling in Bank Direct Marketing","display_name":"A Cluster-Based Data Balancing Ensemble Classifier for Response Modeling in Bank Direct Marketing","publication_year":2015,"publication_date":"2015-12-01","ids":{"openalex":"https://openalex.org/W2220584357","doi":"https://doi.org/10.1142/s1469026815500224","mag":"2220584357"},"language":"en","primary_location":{"id":"doi:10.1142/s1469026815500224","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s1469026815500224","pdf_url":null,"source":{"id":"https://openalex.org/S206936884","display_name":"International Journal of Computational Intelligence and Applications","issn_l":"1469-0268","issn":["1469-0268","1757-5885"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311754","host_organization_name":"Imperial College Press","host_organization_lineage":["https://openalex.org/P4310311754"],"host_organization_lineage_names":["Imperial College Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Intelligence and Applications","raw_type":"journal-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/A5100669288","display_name":"Mohammad Amini","orcid":"https://orcid.org/0009-0000-0197-2731"},"institutions":[{"id":"https://openalex.org/I67009956","display_name":"Iran University of Science and Technology","ror":"https://ror.org/01jw2p796","country_code":"IR","type":"education","lineage":["https://openalex.org/I67009956"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Mohammad Amini","raw_affiliation_strings":["Department of Information Technology School of Industrial Engineering Iran University of Science and Technology Tehran, Postal Code 16846-13114, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Technology School of Industrial Engineering Iran University of Science and Technology Tehran, Postal Code 16846-13114, Iran","institution_ids":["https://openalex.org/I67009956"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079844971","display_name":"Jalal Rezaeenour","orcid":"https://orcid.org/0000-0002-3759-2607"},"institutions":[{"id":"https://openalex.org/I184468904","display_name":"University of Qom","ror":"https://ror.org/03ddeer04","country_code":"IR","type":"education","lineage":["https://openalex.org/I184468904"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Jalal Rezaeenour","raw_affiliation_strings":["Department of Industrial Engineering School of Technology and Engineering University of Qom, Alghadir Blvd. Qom, Postal Code 3716146611, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering School of Technology and Engineering University of Qom, Alghadir Blvd. Qom, Postal Code 3716146611, Iran","institution_ids":["https://openalex.org/I184468904"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011810327","display_name":"Esmaeil Hadavandi","orcid":"https://orcid.org/0000-0002-0002-3304"},"institutions":[{"id":"https://openalex.org/I184468904","display_name":"University of Qom","ror":"https://ror.org/03ddeer04","country_code":"IR","type":"education","lineage":["https://openalex.org/I184468904"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Esmaeil Hadavandi","raw_affiliation_strings":["Department of Industrial Engineering School of Technology and Engineering University of Qom, Alghadir Blvd. Qom, Postal Code 3716146611, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering School of Technology and Engineering University of Qom, Alghadir Blvd. Qom, Postal Code 3716146611, Iran","institution_ids":["https://openalex.org/I184468904"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7727,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.89456127,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"14","issue":"04","first_page":"1550022","last_page":"1550022"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"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/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9930999875068665,"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/direct-marketing","display_name":"Direct marketing","score":0.8435931205749512},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.834670901298523},{"id":"https://openalex.org/keywords/respondent","display_name":"Respondent","score":0.7942146062850952},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7318463325500488},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6138803362846375},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5981957316398621},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5733429789543152},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.572169840335846},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.4265343248844147},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4198814034461975},{"id":"https://openalex.org/keywords/k-means-clustering","display_name":"k-means clustering","score":0.415372371673584},{"id":"https://openalex.org/keywords/marketing","display_name":"Marketing","score":0.1137937605381012}],"concepts":[{"id":"https://openalex.org/C536005652","wikidata":"https://www.wikidata.org/wiki/Q677073","display_name":"Direct marketing","level":2,"score":0.8435931205749512},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.834670901298523},{"id":"https://openalex.org/C2776640315","wikidata":"https://www.wikidata.org/wiki/Q7315941","display_name":"Respondent","level":2,"score":0.7942146062850952},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7318463325500488},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6138803362846375},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5981957316398621},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5733429789543152},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.572169840335846},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.4265343248844147},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4198814034461975},{"id":"https://openalex.org/C207968372","wikidata":"https://www.wikidata.org/wiki/Q310401","display_name":"k-means clustering","level":3,"score":0.415372371673584},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.1137937605381012},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s1469026815500224","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s1469026815500224","pdf_url":null,"source":{"id":"https://openalex.org/S206936884","display_name":"International Journal of Computational Intelligence and Applications","issn_l":"1469-0268","issn":["1469-0268","1757-5885"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311754","host_organization_name":"Imperial College Press","host_organization_lineage":["https://openalex.org/P4310311754"],"host_organization_lineage_names":["Imperial College Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Intelligence and Applications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W391985582","https://openalex.org/W1501500081","https://openalex.org/W1534477342","https://openalex.org/W1582290234","https://openalex.org/W1605695115","https://openalex.org/W1972805005","https://openalex.org/W1977556410","https://openalex.org/W1981399499","https://openalex.org/W1983330696","https://openalex.org/W2001971142","https://openalex.org/W2009185755","https://openalex.org/W2015122554","https://openalex.org/W2017202337","https://openalex.org/W2024924759","https://openalex.org/W2026219386","https://openalex.org/W2038112892","https://openalex.org/W2044861704","https://openalex.org/W2046804081","https://openalex.org/W2052065931","https://openalex.org/W2052549685","https://openalex.org/W2057830095","https://openalex.org/W2061881231","https://openalex.org/W2077575809","https://openalex.org/W2084347746","https://openalex.org/W2111837988","https://openalex.org/W2130486630","https://openalex.org/W2142720090","https://openalex.org/W2147564534","https://openalex.org/W2158275940","https://openalex.org/W2158682393","https://openalex.org/W2163629285","https://openalex.org/W2170505850","https://openalex.org/W2787894218","https://openalex.org/W3004732066","https://openalex.org/W3123614577","https://openalex.org/W4212883601","https://openalex.org/W4250974376","https://openalex.org/W4251708881"],"related_works":["https://openalex.org/W2794896638","https://openalex.org/W2891633941","https://openalex.org/W4390905871","https://openalex.org/W3202800081","https://openalex.org/W3124390867","https://openalex.org/W3101614107","https://openalex.org/W1909207154","https://openalex.org/W1514365828","https://openalex.org/W4390971112","https://openalex.org/W3036530763"],"abstract_inverted_index":{"The":[0,131,178],"aim":[1],"of":[2,68,127,133,170,190],"direct":[3,196],"marketing":[4,18,149,197],"is":[5,32,94,154],"to":[6,15,17,23,34,60,105,140],"find":[7],"the":[8,65,83,100,122,161,168,188,191],"right":[9],"customers":[10,26,36],"who":[11],"are":[12,27,136,163],"most":[13,28],"likely":[14],"respond":[16],"campaign":[19],"messages.":[20],"In":[21,109],"order":[22,139],"detect":[24],"which":[25,118],"valuable,":[29],"response":[30,72,78,192,204],"modeling":[31,73],"used":[33,59],"classify":[35],"as":[37],"respondent":[38],"or":[39,46],"non-respondent":[40,90],"using":[41,124],"their":[42,106],"purchase":[43],"history":[44],"information":[45],"other":[47],"behavioral":[48],"characteristics.":[49],"Data":[50],"mining":[51],"techniques,":[52],"including":[53],"effective":[54],"classification":[55,116,158],"methods,":[56],"can":[57,186],"be":[58,87],"predict":[61],"responsive":[62],"customers.":[63,91],"However,":[64],"inherent":[66],"problem":[67,93],"imbalanced":[69],"data":[70,145],"in":[71,121,138],"brings":[74],"some":[75],"difficulties":[76],"into":[77],"prediction.":[79],"As":[80],"a":[81,125,147],"result,":[82],"prediction":[84,200],"models":[85,97,193],"will":[86],"biased":[88],"towards":[89],"Another":[92],"that":[95,182],"single":[96],"cannot":[98],"provide":[99],"desired":[101],"high":[102],"accuracy":[103,201],"due":[104],"internal":[107],"limitations.":[108],"this":[110,151,171],"paper,":[111],"we":[112],"propose":[113],"an":[114],"ensemble":[115,152,172],"method":[117,153,173,185],"removes":[119],"imbalance":[120],"data,":[123],"combination":[126],"clustering":[128],"and":[129,160,202],"under-sampling.":[130],"predictions":[132],"multiple":[134],"classifiers":[135],"combined":[137],"achieve":[141],"better":[142],"results.":[143],"Using":[144],"from":[146],"bank\u2019s":[148],"campaigns,":[150],"implemented":[155],"on":[156],"different":[157],"techniques":[159],"results":[162,180],"evaluated.":[164],"We":[165],"also":[166],"evaluate":[167],"performance":[169,189],"against":[174],"two":[175],"alternative":[176],"ensembles.":[177],"experimental":[179],"demonstrate":[181],"our":[183],"proposed":[184],"improve":[187],"for":[194],"bank":[195],"by":[198],"raising":[199],"increasing":[203],"rate.":[205]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}