{"id":"https://openalex.org/W2121855758","doi":"https://doi.org/10.1109/icsmc.2009.5346166","title":"A novel and convenient variable selection method for choosing effective input variables for telecommunication customer churn prediction model","display_name":"A novel and convenient variable selection method for choosing effective input variables for telecommunication customer churn prediction model","publication_year":2009,"publication_date":"2009-10-01","ids":{"openalex":"https://openalex.org/W2121855758","doi":"https://doi.org/10.1109/icsmc.2009.5346166","mag":"2121855758"},"language":"en","primary_location":{"id":"doi:10.1109/icsmc.2009.5346166","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsmc.2009.5346166","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Conference on Systems, Man and Cybernetics","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/A5063961362","display_name":"Jiayin Qi","orcid":"https://orcid.org/0000-0001-7162-4898"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiayin Qi","raw_affiliation_strings":["School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China","School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, P. R. China#TAB#"],"affiliations":[{"raw_affiliation_string":"School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, P. R. China#TAB#","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012227564","display_name":"Yuanquan Li","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanquan Li","raw_affiliation_strings":["School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China","School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, P. R. China#TAB#"],"affiliations":[{"raw_affiliation_string":"School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, P. R. China#TAB#","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5063961362"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.2440273,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"3217","last_page":"3222"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9921000003814697,"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/T11536","display_name":"Consumer Retail Behavior Studies","score":0.9675999879837036,"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/computer-science","display_name":"Computer science","score":0.7667587399482727},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.5746057629585266},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5710960626602173},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5304937362670898},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.48287490010261536},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.44874638319015503},{"id":"https://openalex.org/keywords/variables","display_name":"Variables","score":0.44507065415382385},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.43504437804222107},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.41074150800704956},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.31531211733818054},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.11324253678321838},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.09269064664840698},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08783760666847229}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7667587399482727},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.5746057629585266},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5710960626602173},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5304937362670898},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.48287490010261536},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.44874638319015503},{"id":"https://openalex.org/C27574286","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Variables","level":2,"score":0.44507065415382385},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.43504437804222107},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.41074150800704956},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31531211733818054},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.11324253678321838},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.09269064664840698},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08783760666847229},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icsmc.2009.5346166","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsmc.2009.5346166","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Conference on Systems, Man and Cybernetics","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":16,"referenced_works":["https://openalex.org/W1517879152","https://openalex.org/W1530646585","https://openalex.org/W1966021193","https://openalex.org/W1969601879","https://openalex.org/W2057292389","https://openalex.org/W2069300565","https://openalex.org/W2109601635","https://openalex.org/W2125229282","https://openalex.org/W2129957127","https://openalex.org/W2138906630","https://openalex.org/W2142752589","https://openalex.org/W2161560928","https://openalex.org/W2169714367","https://openalex.org/W2293994416","https://openalex.org/W6679728998","https://openalex.org/W6685011964"],"related_works":["https://openalex.org/W2380784125","https://openalex.org/W3004057759","https://openalex.org/W2889085215","https://openalex.org/W2914806737","https://openalex.org/W2998216295","https://openalex.org/W2083862258","https://openalex.org/W4311072340","https://openalex.org/W4375841781","https://openalex.org/W4282583532","https://openalex.org/W4256076151"],"abstract_inverted_index":{"Customer":[0],"churn":[1,42,59],"prediction":[2,29,43],"model":[3],"is":[4],"hot":[5],"research":[6,33],"topic":[7],"in":[8],"recent":[9],"years.":[10],"Most":[11],"of":[12,37],"the":[13,28,35,41,56,67,75,81],"researchers":[14],"have":[15],"paid":[16],"much":[17],"attention":[18],"on":[19,48],"how":[20,49],"to":[21,50,65],"construct":[22],"novelist":[23],"data":[24,82],"mining":[25],"algorithm":[26],"for":[27,40,55],"model,":[30],"while":[31],"less":[32],"concerns":[34],"choosing":[36],"input":[38,53,68],"variables":[39,54,69],"model.":[44,60],"This":[45],"paper":[46],"focuses":[47],"select":[51,66],"effective":[52],"telecommunications":[57],"customer":[58],"We":[61],"proposed":[62],"a":[63],"procedure":[64],"step":[70],"by":[71,77],"step,":[72],"and":[73],"proved":[74],"effect":[76],"comparative":[78],"experiment":[79],"using":[80],"from":[83],"one":[84],"telecom":[85],"carrier.":[86]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}