{"id":"https://openalex.org/W4200458019","doi":"https://doi.org/10.1109/icdabi53623.2021.9655792","title":"Customer Churn Prediction in Telecom Sector with Machine Learning and Information Gain Filter Feature Selection Algorithms","display_name":"Customer Churn Prediction in Telecom Sector with Machine Learning and Information Gain Filter Feature Selection Algorithms","publication_year":2021,"publication_date":"2021-10-25","ids":{"openalex":"https://openalex.org/W4200458019","doi":"https://doi.org/10.1109/icdabi53623.2021.9655792"},"language":"en","primary_location":{"id":"doi:10.1109/icdabi53623.2021.9655792","doi":"https://doi.org/10.1109/icdabi53623.2021.9655792","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdabi53623.2021.9655792","pdf_url":null,"source":{"id":"https://openalex.org/S4363608222","display_name":"International Conference on Data Analytics for Business and Industry (ICDABI)","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":"2021 International Conference on Data Analytics for Business and Industry (ICDABI)","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/A5057218982","display_name":"Yakub Kayode Saheed","orcid":"https://orcid.org/0000-0002-0804-0707","observed_orcids":["https://orcid.org/0000-0002-0804-0707"]},"institutions":[{"id":"https://openalex.org/I65734018","display_name":"American University of Nigeria","ror":"https://ror.org/02kv8bx48","country_code":"NG","type":"education","lineage":["https://openalex.org/I65734018"]}],"countries":["NG"],"is_corresponding":false,"raw_author_name":"Yakub K. Saheed","raw_affiliation_strings":["American University of Nigeria, Yola, Nigeria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"American University of Nigeria, Yola, Nigeria","institution_ids":["https://openalex.org/I65734018"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079650217","display_name":"Moshood Abiola Hambali","orcid":"https://orcid.org/0000-0002-2194-9376","observed_orcids":["https://orcid.org/0000-0002-2194-9376"]},"institutions":[{"id":"https://openalex.org/I4400600929","display_name":"Federal University Wukari","ror":"https://ror.org/04t8bw757","country_code":"NG","type":"education","lineage":["https://openalex.org/I4400600929"]}],"countries":["NG"],"is_corresponding":false,"raw_author_name":"Moshood A. Hambali","raw_affiliation_strings":["Federal University Wukari, Wukari, Nigeria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Federal University Wukari, Wukari, Nigeria","institution_ids":["https://openalex.org/I4400600929"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":14.4149,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.99332162,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"208","last_page":"213"},"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/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"}},{"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"}}],"keywords":[{"id":"https://openalex.org/keywords/customer-churn-prediction","display_name":"customer churn prediction","score":0.9990000128746033},{"id":"https://openalex.org/keywords/telecommunications-customers","display_name":"telecommunications customers","score":0.8169999718666077},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.9039999842643738},{"id":"https://openalex.org/keywords/information-gain","display_name":"information gain","score":0.9430000185966492},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.9359999895095825},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.9300000071525574},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"multilayer perceptron","score":0.8859999775886536},{"id":"https://openalex.org/keywords/naive-bayes","display_name":"naive Bayes","score":0.9570000171661377},{"id":"https://openalex.org/keywords/customer-retention","display_name":"customer retention","score":0.6890000104904175}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7579058408737183},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6791269183158875},{"id":"https://openalex.org/C2983203078","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Information gain","level":2,"score":0.6097375154495239},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5552446842193604},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5111306309700012},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48483192920684814},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4681781828403473},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41693204641342163},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36713355779647827},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.348807156085968},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33371397852897644},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdabi53623.2021.9655792","doi":"https://doi.org/10.1109/icdabi53623.2021.9655792","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdabi53623.2021.9655792","pdf_url":null,"source":{"id":"https://openalex.org/S4363608222","display_name":"International Conference on Data Analytics for Business and Industry (ICDABI)","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":"2021 International Conference on Data Analytics for Business and Industry (ICDABI)","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":0.428600013256073}],"sustainable_development_goals_aurora":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4300000071525574}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321167","display_name":"University of Bahrain","ror":"https://ror.org/0317ekv86"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W126100224","https://openalex.org/W1980462493","https://openalex.org/W1981755567","https://openalex.org/W1984160940","https://openalex.org/W1988160630","https://openalex.org/W1998320186","https://openalex.org/W2017413252","https://openalex.org/W2047562908","https://openalex.org/W2054640944","https://openalex.org/W2059284263","https://openalex.org/W2067594023","https://openalex.org/W2074780813","https://openalex.org/W2115357554","https://openalex.org/W2115555003","https://openalex.org/W2152518947","https://openalex.org/W2161560928","https://openalex.org/W2321670847","https://openalex.org/W2338342538","https://openalex.org/W2560738241","https://openalex.org/W2565861041","https://openalex.org/W2789071465","https://openalex.org/W2894483766","https://openalex.org/W2918396494","https://openalex.org/W2997032147","https://openalex.org/W2997601537","https://openalex.org/W3103404646","https://openalex.org/W3202088707","https://openalex.org/W4253352004","https://openalex.org/W4394224089"],"related_works":["https://openalex.org/W7165600580","https://openalex.org/W4390485458","https://openalex.org/W4392668237","https://openalex.org/W6990150272","https://openalex.org/W4391576473","https://openalex.org/W7213554125","https://openalex.org/W4408018020","https://openalex.org/W4412903569","https://openalex.org/W4405635511","https://openalex.org/W4408477196"],"abstract_inverted_index":{"Customer":[0],"churn":[1,68],"is":[2,48,63,160],"a":[3,67],"significant":[4],"issue":[5],"and":[6,111,129,140,162,178,188],"one":[7],"of":[8,12,66,91,155,165,186],"the":[9,17,25,41,52,64,98,103,107,126,134,137,175],"primary":[10,61],"worries":[11],"large":[13],"businesses.":[14],"Due":[15],"to":[16,31,45,50,55,75,81],"direct":[18],"impact":[19],"on":[20],"firms\u2019":[21],"earnings,":[22],"particularly":[23],"in":[24,86,184],"telecommunications":[26],"sector,":[27],"companies":[28],"are":[29,79,144],"striving":[30],"create":[32],"methods":[33,177],"for":[34,121],"predicting":[35],"probable":[36],"customer":[37],"churn.":[38,58,82],"Thus,":[39],"identifying":[40],"variables":[42],"that":[43,71,124],"contribute":[44],"client":[46],"turnover":[47],"critical":[49],"take":[51],"required":[53],"steps":[54],"reduce":[56],"this":[57,87],"Our":[59],"work\u2019s":[60],"contribution":[62],"development":[65],"prediction":[69],"model":[70,84],"enables":[72],"telecom":[73],"carriers":[74],"forecast":[76],"which":[77],"customers":[78],"likely":[80],"The":[83,150,170],"created":[85],"work":[88],"makes":[89],"use":[90],"machine":[92],"learning":[93],"(ML)":[94],"approaches":[95],"such":[96],"as":[97],"Support":[99],"Vector":[100],"Machine":[101],"(SVM),":[102],"Multi-Layer":[104],"Perceptron":[105],"(MLP),":[106],"Random":[108],"Forest":[109],"(RF),":[110],"Naive":[112],"Bayes":[113],"(NB).":[114],"This":[115],"paper":[116],"proposes":[117],"an":[118,153,163],"innovative":[119],"technique":[120],"feature":[122,158,168],"selection":[123,159],"combines":[125],"Information":[127],"Gain":[128],"Ranker":[130],"methods.":[131],"To":[132],"evaluate":[133],"model\u2019s":[135],"performance,":[136],"accuracy,":[138],"precision,":[139],"F-measure":[141],"standard":[142],"measures":[143],"used,":[145],"along":[146],"with":[147,174],"10-fold":[148],"cross-validation.":[149],"findings":[151],"gave":[152],"accuracy":[154,164],"95.02%":[156],"when":[157],"considered":[161],"92.92%":[166],"without":[167],"selection.":[169],"results":[171],"were":[172],"compared":[173],"existing":[176],"our":[179],"models":[180],"showed":[181],"competitive":[182],"performance":[183],"terms":[185],"precision":[187],"F-measure.":[189]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":6}],"updated_date":"2026-10-27T15:51:10.671819","created_date":"2021-12-31T00:00:00"}
