{"id":"https://openalex.org/W4282836328","doi":"https://doi.org/10.1109/icdcece53908.2022.9793315","title":"Feature Selection for Customer Churn Prediction: A Review on the Methods & Techniques applied in the Telecom Industry","display_name":"Feature Selection for Customer Churn Prediction: A Review on the Methods & Techniques applied in the Telecom Industry","publication_year":2022,"publication_date":"2022-04-23","ids":{"openalex":"https://openalex.org/W4282836328","doi":"https://doi.org/10.1109/icdcece53908.2022.9793315"},"language":"en","primary_location":{"id":"doi:10.1109/icdcece53908.2022.9793315","doi":"https://doi.org/10.1109/icdcece53908.2022.9793315","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdcece53908.2022.9793315","pdf_url":null,"source":{"id":"https://openalex.org/S7407088644","display_name":"International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","raw_type":"proceedings-article"},"volume":null,"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/A5014625870","display_name":"Yuwadi Thein Naing","orcid":"https://orcid.org/0000-0001-7842-0927","observed_orcids":["https://orcid.org/0000-0001-7842-0927"]},"institutions":[{"id":"https://openalex.org/I181397559","display_name":"Asia Pacific University of Technology & Innovation","ror":"https://ror.org/03c52a632","country_code":"MY","type":"education","lineage":["https://openalex.org/I181397559"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Yuwadi Thein Naing","raw_affiliation_strings":["Asia Pacific University of Technology & Innovation,Kuala Lumpur,Malaysia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Asia Pacific University of Technology & Innovation,Kuala Lumpur,Malaysia","institution_ids":["https://openalex.org/I181397559"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042083709","display_name":"Mafas Raheem","orcid":"https://orcid.org/0000-0001-8584-3493","observed_orcids":["https://orcid.org/0000-0001-8584-3493"]},"institutions":[{"id":"https://openalex.org/I181397559","display_name":"Asia Pacific University of Technology & Innovation","ror":"https://ror.org/03c52a632","country_code":"MY","type":"education","lineage":["https://openalex.org/I181397559"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Mafas Raheem","raw_affiliation_strings":["Asia Pacific University of Technology & Innovation,Kuala Lumpur,Malaysia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Asia Pacific University of Technology & Innovation,Kuala Lumpur,Malaysia","institution_ids":["https://openalex.org/I181397559"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078635305","display_name":"Nowshath Kadhar Batcha","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I181397559","display_name":"Asia Pacific University of Technology & Innovation","ror":"https://ror.org/03c52a632","country_code":"MY","type":"education","lineage":["https://openalex.org/I181397559"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Nowshath K Batcha","raw_affiliation_strings":["Asia Pacific University of Technology & Innovation,Kuala Lumpur,Malaysia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Asia Pacific University of Technology & Innovation,Kuala Lumpur,Malaysia","institution_ids":["https://openalex.org/I181397559"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I181397559"],"apc_list":null,"apc_paid":null,"fwci":6.3584,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.96726619,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":1.0,"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":1.0,"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/T10154","display_name":"Customer Service Quality and Loyalty","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1407","display_name":"Organizational Behavior and Human Resource Management"},"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/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.0,"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/feature-selection","display_name":"feature selection","score":0.8539999723434448},{"id":"https://openalex.org/keywords/customer-churn-prediction","display_name":"customer churn prediction","score":1.0},{"id":"https://openalex.org/keywords/customer-retention","display_name":"customer retention","score":0.7590000033378601},{"id":"https://openalex.org/keywords/telecommunications-industry","display_name":"telecommunications industry","score":0.8539999723434448},{"id":"https://openalex.org/keywords/filter-methods","display_name":"filter methods","score":0.6980000138282776},{"id":"https://openalex.org/keywords/wrapper-methods","display_name":"wrapper methods","score":0.9990000128746033},{"id":"https://openalex.org/keywords/embedded-methods","display_name":"embedded methods","score":0.996999979019165},{"id":"https://openalex.org/keywords/fisher-score","display_name":"Fisher score","score":0.8550000190734863},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.9269999861717224},{"id":"https://openalex.org/keywords/data-preparation","display_name":"data preparation","score":0.5099999904632568}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7676275968551636},{"id":"https://openalex.org/C127576917","wikidata":"https://www.wikidata.org/wiki/Q624630","display_name":"Competitor analysis","level":2,"score":0.6817683577537537},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6661237478256226},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6060495376586914},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5717990398406982},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5156256556510925},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.49977755546569824},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4736957550048828},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.46148931980133057},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3604171872138977},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.3471793234348297},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.346238374710083},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.2157590389251709},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.1841977834701538},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdcece53908.2022.9793315","doi":"https://doi.org/10.1109/icdcece53908.2022.9793315","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdcece53908.2022.9793315","pdf_url":null,"source":{"id":"https://openalex.org/S7407088644","display_name":"International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2558749735","https://openalex.org/W2594914212","https://openalex.org/W2606176882","https://openalex.org/W2742070444","https://openalex.org/W2792328488","https://openalex.org/W2909922888","https://openalex.org/W2914716244","https://openalex.org/W2939289155","https://openalex.org/W2992634800","https://openalex.org/W2997802692","https://openalex.org/W3001120959","https://openalex.org/W3016662809","https://openalex.org/W3031101592","https://openalex.org/W3032007873","https://openalex.org/W3032811227","https://openalex.org/W3045717404","https://openalex.org/W3097606459","https://openalex.org/W3101415116","https://openalex.org/W3117214587"],"related_works":["https://openalex.org/W4200458019","https://openalex.org/W4396751866","https://openalex.org/W3005818994","https://openalex.org/W3174086514","https://openalex.org/W1975430255","https://openalex.org/W4394953989","https://openalex.org/W2120757798","https://openalex.org/W7082635909","https://openalex.org/W4310500583","https://openalex.org/W4392668237"],"abstract_inverted_index":{"The":[0,155,172],"rapid":[1],"growth":[2],"of":[3,13,28,35,69,112],"the":[4,11,21,29,33,48,58,70,106,138],"telecommunication":[5],"industry":[6],"has":[7],"significantly":[8],"contributed":[9],"to":[10,46,57],"rise":[12],"interest":[14],"in":[15,74,90,179],"customer":[16,66],"churn":[17,78],"prediction":[18,79],"studies":[19,92,101,125],"within":[20,93],"telecom":[22],"domain.":[23,95],"Customer":[24],"retention":[25],"is":[26,42,62,81,87,186,195],"one":[27],"significant":[30],"factors":[31],"affecting":[32],"livelihood":[34],"businesses":[36],"across":[37],"every":[38],"industry.":[39],"Therefore,":[40],"it":[41],"many":[43,91],"companies\u2019":[44],"priority":[45],"safeguard":[47],"existing":[49],"customers,":[50],"from":[51,116],"cancelling":[52],"their":[53,113],"subscription":[54],"and":[55,122,151,169],"migrating":[56],"competitors\u2019":[59],"company,":[60],"which":[61,185,194],"also":[63],"known":[64],"as":[65,110],"churn.":[67],"One":[68],"most":[71,173],"crucial":[72],"processes":[73],"achieving":[75],"a":[76,187],"high-performance":[77],"model":[80],"feature":[82,107,131,145,149,153,157,175],"selection.":[83,154],"Unfortunately,":[84],"this":[85,94,180],"process":[86,109,143],"not":[88,103],"highlighted":[89],"It":[96],"was":[97,182],"found":[98,178],"that":[99],"few":[100],"did":[102],"even":[104],"include":[105],"selection":[108,132,146,150,158,176],"part":[111],"data":[114,141],"preparation":[115,142],"several":[117],"articles":[118],"published":[119],"between":[120],"2016":[121],"2021.":[123],"Countable":[124],"collected,":[126],"were":[127],"conducted":[128],"evolving":[129],"around":[130],"by":[133,191],"comparing":[134],"different":[135],"methods.":[136],"Moreover,":[137],"researchers":[139],"whose":[140],"involved":[144],"used":[147],"manual":[148],"automatic":[152,156],"can":[159],"then":[160],"be":[161],"further":[162],"classified":[163],"into":[164],"filter":[165,188],"method,":[166,189],"wrapper":[167],"method":[168,177],"embedded":[170,197],"method.":[171,198],"prominent":[174],"survey":[181],"Fisher":[183],"Score,":[184],"followed":[190],"Random":[192],"Forest,":[193],"an":[196]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1}],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2022-06-15T00:00:00"}