{"id":"https://openalex.org/W2625660771","doi":"https://doi.org/10.1145/3055635.3056615","title":"Predicting Classification of Telecommunication Subscribers Based on Polytomous Logistic Regression Usage Model","display_name":"Predicting Classification of Telecommunication Subscribers Based on Polytomous Logistic Regression Usage Model","publication_year":2017,"publication_date":"2017-02-24","ids":{"openalex":"https://openalex.org/W2625660771","doi":"https://doi.org/10.1145/3055635.3056615","mag":"2625660771"},"language":"en","primary_location":{"id":"doi:10.1145/3055635.3056615","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3055635.3056615","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Machine Learning and Computing","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/A5052711150","display_name":"Rosmina Joy Cabauatan","orcid":null},"institutions":[{"id":"https://openalex.org/I95350115","display_name":"Technological Institute of the Philippines","ror":"https://ror.org/02p79p790","country_code":"PH","type":"education","lineage":["https://openalex.org/I95350115"]}],"countries":["PH"],"is_corresponding":true,"raw_author_name":"Rosmina Joy M. Cabauatan","raw_affiliation_strings":["Technological Institute of the Philippines, Quezon City, Philippines"],"affiliations":[{"raw_affiliation_string":"Technological Institute of the Philippines, Quezon City, Philippines","institution_ids":["https://openalex.org/I95350115"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001161800","display_name":"Bobby D. Gerardo","orcid":"https://orcid.org/0000-0001-8516-3850"},"institutions":[{"id":"https://openalex.org/I888069255","display_name":"West Visayas State University","ror":"https://ror.org/02wbzep73","country_code":"PH","type":"education","lineage":["https://openalex.org/I888069255"]}],"countries":["PH"],"is_corresponding":false,"raw_author_name":"Bobby D. Gerardo","raw_affiliation_strings":["West Visayas State University, Iloilo City, Philippines"],"affiliations":[{"raw_affiliation_string":"West Visayas State University, Iloilo City, Philippines","institution_ids":["https://openalex.org/I888069255"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5052711150"],"corresponding_institution_ids":["https://openalex.org/I95350115"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.08571735,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"258","last_page":"267"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9994999766349792,"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":0.9994999766349792,"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":0.98580002784729,"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":0.9805999994277954,"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/polytomous-rasch-model","display_name":"Polytomous Rasch model","score":0.9203211069107056},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.7774159908294678},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6841995120048523},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6183394193649292},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4783558249473572},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.46654200553894043},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4597800374031067},{"id":"https://openalex.org/keywords/odds","display_name":"Odds","score":0.4552955627441406},{"id":"https://openalex.org/keywords/telecommunications-service","display_name":"Telecommunications service","score":0.4463297426700592},{"id":"https://openalex.org/keywords/profit","display_name":"Profit (economics)","score":0.4330856204032898},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.41674959659576416},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4105222523212433},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.379167377948761},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3391343355178833},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18523165583610535},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.14243340492248535},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.12937116622924805},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.08790186047554016}],"concepts":[{"id":"https://openalex.org/C207968926","wikidata":"https://www.wikidata.org/wiki/Q7227107","display_name":"Polytomous Rasch model","level":4,"score":0.9203211069107056},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.7774159908294678},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6841995120048523},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6183394193649292},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4783558249473572},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.46654200553894043},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4597800374031067},{"id":"https://openalex.org/C143095724","wikidata":"https://www.wikidata.org/wiki/Q515895","display_name":"Odds","level":3,"score":0.4552955627441406},{"id":"https://openalex.org/C33271190","wikidata":"https://www.wikidata.org/wiki/Q3467747","display_name":"Telecommunications service","level":2,"score":0.4463297426700592},{"id":"https://openalex.org/C181622380","wikidata":"https://www.wikidata.org/wiki/Q26911","display_name":"Profit (economics)","level":2,"score":0.4330856204032898},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.41674959659576416},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4105222523212433},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.379167377948761},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3391343355178833},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18523165583610535},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.14243340492248535},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.12937116622924805},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.08790186047554016},{"id":"https://openalex.org/C171606756","wikidata":"https://www.wikidata.org/wiki/Q506132","display_name":"Psychometrics","level":2,"score":0.0},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C19875794","wikidata":"https://www.wikidata.org/wiki/Q1207340","display_name":"Item response theory","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3055635.3056615","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3055635.3056615","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1910757327","https://openalex.org/W1984309262","https://openalex.org/W2082729474","https://openalex.org/W2140190241","https://openalex.org/W2186428165","https://openalex.org/W2337693769","https://openalex.org/W2496359543","https://openalex.org/W2528251263","https://openalex.org/W2581455004","https://openalex.org/W2912323841"],"related_works":["https://openalex.org/W2147290304","https://openalex.org/W2360384556","https://openalex.org/W1596648190","https://openalex.org/W146579346","https://openalex.org/W3044856159","https://openalex.org/W306109848","https://openalex.org/W7009195","https://openalex.org/W2160900637","https://openalex.org/W28566455","https://openalex.org/W4248448931"],"abstract_inverted_index":{"Validating":[0],"the":[1,29,48,68,89,92,97],"performance":[2],"of":[3,20,31,39,44,50,52,58,62,91,106,112,118],"data":[4,8],"mining":[5],"models":[6,35,98],"requires":[7],"not":[9],"seen":[10],"during":[11],"model":[12],"creation.":[13],"In":[14],"this":[15],"paper,":[16],"a":[17,21,103,114],"bootstrapped":[18],"partition":[19],"full":[22],"dataset":[23],"has":[24],"been":[25],"used":[26,73],"to":[27,55,124,129],"gauge":[28],"ability":[30],"polytomous":[32],"logistic":[33,63],"regression":[34,64],"in":[36],"predicting":[37],"classifications":[38],"telecommunication":[40],"subscribers.":[41],"Coded":[42],"coefficients":[43],"significant":[45],"predictors":[46],"facilitated":[47],"calculation":[49],"log":[51],"odds":[53],"attributed":[54],"each":[56],"group":[57,77],"users":[59],"and":[60,86,109],"formulation":[61],"equations.":[65],"Converted":[66],"from":[67,83],"results":[69],"are":[70],"executed":[71],"probabilities":[72],"for":[74,116],"obtaining":[75],"distinct":[76],"memberships.":[78],"The":[79],"classification":[80],"rate":[81],"obtained":[82],"cross-tabulated":[84],"counts":[85],"percentages":[87],"confirmed":[88],"capability":[90],"model.":[93],"With":[94],"accurate":[95],"predictions,":[96],"could":[99],"provide":[100],"decision":[101],"makers":[102],"better":[104],"understanding":[105],"individual":[107],"interactions":[108],"group-level":[110],"behaviors":[111],"subscribers,":[113],"tipoff":[115],"improvement":[117],"Short":[119],"Message":[120],"Service":[121],"(SMS)":[122],"schemes":[123],"positively":[125],"leverage":[126],"service":[127],"usage":[128],"generate":[130],"more":[131],"profit":[132],"while":[133],"extending":[134],"customer":[135],"satisfaction.":[136]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}