{"id":"https://openalex.org/W2169714367","doi":"https://doi.org/10.1109/icsmc.2007.4414197","title":"A hybrid KNN-LR classifier and its application in customer churn prediction","display_name":"A hybrid KNN-LR classifier and its application in customer churn prediction","publication_year":2007,"publication_date":"2007-01-01","ids":{"openalex":"https://openalex.org/W2169714367","doi":"https://doi.org/10.1109/icsmc.2007.4414197","mag":"2169714367"},"language":"en","primary_location":{"id":"doi:10.1109/icsmc.2007.4414197","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsmc.2007.4414197","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 IEEE International Conference on Systems, Man and Cybernetics","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/A5101398127","display_name":"Yangming Zhang","orcid":"https://orcid.org/0000-0002-7561-4184"},"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":"Yangming Zhang","raw_affiliation_strings":["School of Economics and Management, University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Economics and Management, University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","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":false,"raw_author_name":"Jiayin Qi","raw_affiliation_strings":["School of Economics and Management, University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Economics and Management, University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100531487","display_name":"Huaying Shu","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":"Huaying Shu","raw_affiliation_strings":["School of Economics and Management, University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Economics and Management, University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079044893","display_name":"Jiantong Cao","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":"Jiantong Cao","raw_affiliation_strings":["School of Economics and Management, University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Economics and Management, University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3265","last_page":"3269"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9990000128746033,"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":0.9990000128746033,"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":0.995199978351593,"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/T10057","display_name":"Face and Expression Recognition","score":0.9882000088691711,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/computer-science","display_name":"Computer science","score":0.6908349394798279},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6878984570503235},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.6356419920921326},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5745558738708496},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5279965400695801},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5197517275810242},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.5022003650665283},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.4773595333099365},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4494004249572754},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43462204933166504},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.42502063512802124},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.27284932136535645}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6908349394798279},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6878984570503235},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.6356419920921326},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5745558738708496},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5279965400695801},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5197517275810242},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.5022003650665283},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.4773595333099365},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4494004249572754},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43462204933166504},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.42502063512802124},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.27284932136535645},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icsmc.2007.4414197","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsmc.2007.4414197","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 IEEE International Conference on Systems, Man and Cybernetics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W53285506","https://openalex.org/W1524761913","https://openalex.org/W1527338945","https://openalex.org/W1546647560","https://openalex.org/W1554663460","https://openalex.org/W1833977909","https://openalex.org/W1975625812","https://openalex.org/W1990748933","https://openalex.org/W1995953281","https://openalex.org/W2084812512","https://openalex.org/W2125055259","https://openalex.org/W2145346462","https://openalex.org/W2147169507","https://openalex.org/W2155653793","https://openalex.org/W2161349318","https://openalex.org/W4239899209","https://openalex.org/W4244238212","https://openalex.org/W4388297464","https://openalex.org/W6631531416","https://openalex.org/W6631546530"],"related_works":["https://openalex.org/W4382934300","https://openalex.org/W2121061354","https://openalex.org/W4285388059","https://openalex.org/W2158177428","https://openalex.org/W2165299150","https://openalex.org/W2740479052","https://openalex.org/W4200584542","https://openalex.org/W2346228996","https://openalex.org/W3006113422","https://openalex.org/W3172103400"],"abstract_inverted_index":{"This":[0,39],"paper":[1],"presents":[2],"a":[3,8,29],"hybrid":[4,40],"approach":[5,12,79],"for":[6],"building":[7],"binary":[9],"classifier.":[10],"The":[11,66],"is":[13,95],"the":[14,17,35,44,47,57,69,77,83],"combination":[15],"of":[16,46,68],"k-nearest":[18],"neighbor":[19],"algorithm,":[20],"handling":[21],"separately":[22],"m":[23],"1-dimensional":[24],"data":[25,30,74,108],"sets":[26,75],"divided":[27],"from":[28],"set":[31],"in":[32,50,53,100],"m-dimension,":[33],"and":[34,59,90],"logistic":[36,48],"regression":[37,49],"method.":[38],"KNN-LR":[41],"classifier":[42],"improves":[43],"performance":[45],"classification":[51,85],"accuracy":[52],"some":[54],"situations":[55],"where":[56],"predictor":[58],"target":[60],"variables":[61],"exhibit":[62],"complex":[63],"nonlinear":[64],"relationships.":[65],"results":[67],"experiment":[70],"on":[71,105],"four":[72],"benchmark":[73],"show":[76],"proposed":[78],"compares":[80],"favorably":[81],"with":[82],"well-known":[84],"algorithms":[86],"such":[87],"as":[88],"C4.5":[89],"RBF.":[91],"Furthermore,":[92],"its":[93,98],"effectiveness":[94],"illustrated":[96],"by":[97],"application":[99],"customer":[101,107],"churn":[102],"prediction":[103],"based":[104],"real-world":[106],"sets.":[109]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":4},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
