{"id":"https://openalex.org/W2140166336","doi":"https://doi.org/10.1109/iri.2008.4583069","title":"Preventing customer churn by using random forests modeling","display_name":"Preventing customer churn by using random forests modeling","publication_year":2008,"publication_date":"2008-07-01","ids":{"openalex":"https://openalex.org/W2140166336","doi":"https://doi.org/10.1109/iri.2008.4583069","mag":"2140166336"},"language":"en","primary_location":{"id":"doi:10.1109/iri.2008.4583069","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iri.2008.4583069","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Conference on Information Reuse and Integration","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/A5029626046","display_name":"Weiyun Ying","orcid":null},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiyun Ying","raw_affiliation_strings":["School of Management, Xi''an Jiaotong University, Xi'an, China","School of Management, Xi'an Jiaotong University, 710049 China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Management, Xi''an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]},{"raw_affiliation_string":"School of Management, Xi'an Jiaotong University, 710049 China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082207156","display_name":"Xiu Li","orcid":"https://orcid.org/0000-0001-6906-6735"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiu Li","raw_affiliation_strings":["National CIMS Engineering & Research Center, Tsinghua University, Beijing, China","National CIMS Engineering and Research Center, Tsinghua University, Beijing, 100084, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National CIMS Engineering & Research Center, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"National CIMS Engineering and Research Center, Tsinghua University, Beijing, 100084, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113689151","display_name":"Yaya Xie","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaya Xie","raw_affiliation_strings":["National CIMS Engineering & Research Center, Tsinghua University, Beijing, China","National CIMS Engineering and Research Center, Tsinghua University, Beijing, 100084, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National CIMS Engineering & Research Center, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"National CIMS Engineering and Research Center, Tsinghua University, Beijing, 100084, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108594642","display_name":"Ellis Johnson","orcid":"https://orcid.org/0000-0001-6666-1408"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ellis Johnson","raw_affiliation_strings":["School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA","School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, 30332, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]},{"raw_affiliation_string":"School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, 30332, U.S.A","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4731,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.88989265,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"429","last_page":"434"},"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9966999888420105,"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"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9706000089645386,"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/random-forest","display_name":"Random forest","score":0.8424142599105835},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7597305774688721},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7162474989891052},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.6085972189903259},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5850446224212646},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5472670197486877},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5199217200279236},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.517331063747406},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5159805417060852},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5043953657150269},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.47941964864730835},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.4673583507537842},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07837247848510742}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.8424142599105835},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7597305774688721},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7162474989891052},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6085972189903259},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5850446224212646},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5472670197486877},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5199217200279236},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.517331063747406},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5159805417060852},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5043953657150269},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.47941964864730835},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.4673583507537842},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07837247848510742},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iri.2008.4583069","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iri.2008.4583069","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Conference on Information Reuse and Integration","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.6399999856948853,"display_name":"Life in Land"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W273955616","https://openalex.org/W1535056464","https://openalex.org/W1990421725","https://openalex.org/W1998320186","https://openalex.org/W2050547330","https://openalex.org/W2069300565","https://openalex.org/W2076313627","https://openalex.org/W2096059947","https://openalex.org/W2106817624","https://openalex.org/W2138906630","https://openalex.org/W2140301981","https://openalex.org/W2142752589","https://openalex.org/W2165819032","https://openalex.org/W2357351687","https://openalex.org/W2911964244","https://openalex.org/W6610017368"],"related_works":["https://openalex.org/W4200112873","https://openalex.org/W2955796858","https://openalex.org/W4224941037","https://openalex.org/W2004826645","https://openalex.org/W3135818052","https://openalex.org/W4366990902","https://openalex.org/W4317732970","https://openalex.org/W4388550696","https://openalex.org/W4224922629","https://openalex.org/W4321636153"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"use":[4],"the":[5,12,24,43,51,62,116],"improved":[6],"balanced":[7],"random":[8,26],"forests(IBRF)":[9],"to":[10,28,66,82,114],"predict":[11],"customer":[13,70],"churn,":[14],"while":[15],"integrating":[16],"a":[17,30,67],"sampling":[18,123],"technique":[19],"and":[20,54,98,107,122],"cost-sensitive":[21],"learning":[22],"into":[23],"standard":[25],"forests":[27],"achieve":[29],"better":[31],"performance":[32],"than":[33],"most":[34],"existing":[35],"algorithms.":[36],"The":[37,105],"nature":[38],"of":[39,61,72,109,118],"IBRF":[40],"is":[41],"that":[42],"best":[44],"features":[45],"are":[46,80,112,125],"iteratively":[47],"learned":[48],"by":[49,55],"altering":[50],"class":[52],"distribution":[53],"putting":[56],"higher":[57],"penalties":[58],"on":[59],"misclassification":[60],"minority":[63],"class.":[64],"Applied":[65],"credit":[68],"debt":[69],"database":[71],"an":[73],"anonymous":[74],"commercial":[75],"bank":[76],"in":[77],"China,":[78],"they":[79],"proven":[81],"significantly":[83],"improve":[84],"prediction":[85],"accuracy":[86],"comparing":[87],"with":[88],"other":[89],"algorithms,":[90],"such":[91],"as":[92],"artificial":[93],"neural":[94],"networks,":[95],"decision":[96],"trees,":[97],"class-weighted":[99],"core":[100],"support":[101],"vector":[102],"machines":[103],"(CWC-SVM).":[104],"assessment":[106],"comparison":[108],"these":[110],"algorithms":[111],"made":[113],"analyze":[115],"traits":[117],"them.":[119],"Data":[120],"processing":[121],"scheme":[124],"also":[126],"detailed":[127],"introduced.":[128]},"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":2020,"cited_by_count":6},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}