{"id":"https://openalex.org/W1989450878","doi":"https://doi.org/10.1109/icsmc.2012.6377917","title":"Genetic Programming and Adaboosting based churn prediction for Telecom","display_name":"Genetic Programming and Adaboosting based churn prediction for Telecom","publication_year":2012,"publication_date":"2012-10-01","ids":{"openalex":"https://openalex.org/W1989450878","doi":"https://doi.org/10.1109/icsmc.2012.6377917","mag":"1989450878"},"language":"en","primary_location":{"id":"doi:10.1109/icsmc.2012.6377917","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsmc.2012.6377917","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5055481442","display_name":"Adnan Idris","orcid":"https://orcid.org/0000-0001-6298-1551"},"institutions":[{"id":"https://openalex.org/I134276161","display_name":"Pakistan Institute of Engineering and Applied Sciences","ror":"https://ror.org/04d4mbk19","country_code":"PK","type":"education","lineage":["https://openalex.org/I134276161"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Adnan Idris","raw_affiliation_strings":["Pattern Recognition Laboratory, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan","Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 45650, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pattern Recognition Laboratory, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan","institution_ids":["https://openalex.org/I134276161"]},{"raw_affiliation_string":"Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 45650, Pakistan","institution_ids":["https://openalex.org/I134276161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083112369","display_name":"Asifullah Khan","orcid":"https://orcid.org/0000-0003-2039-5305"},"institutions":[{"id":"https://openalex.org/I134276161","display_name":"Pakistan Institute of Engineering and Applied Sciences","ror":"https://ror.org/04d4mbk19","country_code":"PK","type":"education","lineage":["https://openalex.org/I134276161"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Asifullah Khan","raw_affiliation_strings":["Pattern Recognition Laboratory, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan","Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 45650, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pattern Recognition Laboratory, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan","institution_ids":["https://openalex.org/I134276161"]},{"raw_affiliation_string":"Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 45650, Pakistan","institution_ids":["https://openalex.org/I134276161"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055136874","display_name":"Yeon Soo Lee","orcid":"https://orcid.org/0000-0002-6979-9952"},"institutions":[{"id":"https://openalex.org/I39705031","display_name":"Daegu Catholic University","ror":"https://ror.org/04fxknd68","country_code":"KR","type":"education","lineage":["https://openalex.org/I39705031"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yeon Soo Lee","raw_affiliation_strings":["Department of Biomedical Engineering, College of Medical Science, Catholic University of Daegu, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, College of Medical Science, Catholic University of Daegu, South Korea","institution_ids":["https://openalex.org/I39705031"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.8117,"has_fulltext":false,"cited_by_count":60,"citation_normalized_percentile":{"value":0.90453622,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1328","last_page":"1332"},"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":0.9937999844551086,"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/T11536","display_name":"Consumer Retail Behavior Studies","score":0.9912999868392944,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.7870252132415771},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7290215492248535},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.6908316612243652},{"id":"https://openalex.org/keywords/genetic-programming","display_name":"Genetic programming","score":0.5822163820266724},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.580035924911499},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5390156507492065},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5238996744155884},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.5046435594558716},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.485768586397171},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39582890272140503},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.23470202088356018}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7870252132415771},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7290215492248535},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.6908316612243652},{"id":"https://openalex.org/C110332635","wikidata":"https://www.wikidata.org/wiki/Q629498","display_name":"Genetic programming","level":2,"score":0.5822163820266724},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.580035924911499},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5390156507492065},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5238996744155884},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.5046435594558716},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.485768586397171},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39582890272140503},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.23470202088356018}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icsmc.2012.6377917","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsmc.2012.6377917","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.6200000047683716}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W258036788","https://openalex.org/W1595053282","https://openalex.org/W1608724047","https://openalex.org/W1680540421","https://openalex.org/W1966021193","https://openalex.org/W1990788070","https://openalex.org/W1995026436","https://openalex.org/W2004014581","https://openalex.org/W2017027240","https://openalex.org/W2019335772","https://openalex.org/W2034489756","https://openalex.org/W2061798849","https://openalex.org/W2078704831","https://openalex.org/W2150757437","https://openalex.org/W2158934641","https://openalex.org/W2167114860","https://openalex.org/W2384816640","https://openalex.org/W2601335113","https://openalex.org/W6635541841","https://openalex.org/W6637372486"],"related_works":["https://openalex.org/W2327035729","https://openalex.org/W2348748958","https://openalex.org/W3039673966","https://openalex.org/W1538046993","https://openalex.org/W2884325279","https://openalex.org/W1570592793","https://openalex.org/W1525436954","https://openalex.org/W2385662756","https://openalex.org/W2585372724","https://openalex.org/W2241444561"],"abstract_inverted_index":{"Churn":[0],"prediction":[1,33,94,135,166],"model":[2,30],"guides":[3],"the":[4,10,31,39,53,62,67,75,89,111,116,120,128,155,173],"customer":[5],"relationship":[6],"management":[7],"to":[8,15,29,70,102],"retain":[9],"customers":[11],"who":[12],"are":[13,27,113],"expected":[14],"quit.":[16],"In":[17,51,77],"recent":[18],"times,":[19],"a":[20,46,104,124,149],"number":[21,105],"of":[22,49,61,92,106,127,130,152],"tree":[23],"based":[24,85],"ensemble":[25],"classifiers":[26],"used":[28,101],"churn":[32,93,165],"in":[34,72,95],"telecom.":[35,96],"These":[36],"models":[37],"predict":[38],"churners":[40],"quite":[41],"satisfactorily;":[42],"however,":[43],"there":[44],"is":[45,100,137,157],"considerable":[47],"margin":[48],"improvement.":[50],"telecom,":[52],"enormous":[54],"size,":[55],"imbalanced":[56],"nature,":[57],"and":[58,148],"high":[59],"dimensionality":[60],"training":[63],"dataset":[64],"mainly":[65],"cause":[66],"classification":[68],"algorithms":[69],"suffer":[71],"accurately":[73],"predicting":[74],"churners.":[76],"this":[78],"paper,":[79],"we":[80],"use":[81],"Genetic":[82],"Programming":[83],"(GP)":[84],"approach":[86,167],"for":[87,172],"modeling":[88],"challenging":[90],"problem":[91],"Adaboost":[97],"style":[98],"boosting":[99],"evolve":[103],"programs":[107,118,131],"per":[108,132],"class.":[109,133],"Finally,":[110],"predictions":[112],"made":[114],"with":[115],"resulting":[117],"using":[119,139],"higher":[121],"output,":[122],"from":[123],"weighted":[125],"sum":[126],"outputs":[129],"The":[134],"accuracy":[136],"evaluated":[138],"10":[140],"fold":[141],"cross":[142],"validation":[143],"on":[144],"standard":[145],"telecom":[146,175],"datasets":[147],"0.89":[150],"score":[151],"area":[153],"under":[154],"curve":[156],"observed.":[158],"We":[159],"hope":[160],"that":[161],"such":[162],"an":[163],"efficient":[164],"might":[168],"be":[169],"significantly":[170],"beneficial":[171],"competitive":[174],"industry.":[176]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2026-08-08T01:25:22.217667","created_date":"2025-10-10T00:00:00"}
