{"id":"https://openalex.org/W7220720184","doi":"https://doi.org/10.1109/aidas71914.2026.11712826","title":"Top-K Permutation-Based Feature Selection with Reused One-Hot Subspace and Threshold Optimization for University Student Telecom Churn Prediction","display_name":"Top-K Permutation-Based Feature Selection with Reused One-Hot Subspace and Threshold Optimization for University Student Telecom Churn Prediction","publication_year":2026,"publication_date":"2026-09-01","ids":{"openalex":"https://openalex.org/W7220720184","doi":"https://doi.org/10.1109/aidas71914.2026.11712826"},"language":"en","primary_location":{"id":"doi:10.1109/aidas71914.2026.11712826","doi":"https://doi.org/10.1109/aidas71914.2026.11712826","is_oa":true,"landing_page_url":"https://doi.org/10.1109/aidas71914.2026.11712826","pdf_url":null,"source":{"id":"https://openalex.org/S7407086711","display_name":"International Conference on Artificial Intelligence and Data Sciences (AiDAS)","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 7th International Conference on Artificial Intelligence and Data Sciences (AiDAS)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1109/aidas71914.2026.11712826","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001809543","display_name":"Joe Li","orcid":"https://orcid.org/0009-0009-6834-6810","observed_orcids":["https://orcid.org/0009-0009-6834-6810"]},"institutions":[{"id":"https://openalex.org/I902764099","display_name":"SEGi University","ror":"https://ror.org/05crr5s63","country_code":"MY","type":"education","lineage":["https://openalex.org/I902764099"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Jiejun Li","raw_affiliation_strings":["SEGi University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SEGi University","institution_ids":["https://openalex.org/I902764099"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114190344","display_name":"Norriza Binti Hussin","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I902764099","display_name":"SEGi University","ror":"https://ror.org/05crr5s63","country_code":"MY","type":"education","lineage":["https://openalex.org/I902764099"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Norriza Hussin","raw_affiliation_strings":["SEGi University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SEGi University","institution_ids":["https://openalex.org/I902764099"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5157702368","display_name":"Fengyu Fei","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210164614","display_name":"Jiangmen Polytechnic","ror":"https://ror.org/05v7z3142","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210164614"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fengyu Fei","raw_affiliation_strings":["Jiangmen Polytechnic"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangmen Polytechnic","institution_ids":["https://openalex.org/I4210164614"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112849111","display_name":"Jing Gong","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210164614","display_name":"Jiangmen Polytechnic","ror":"https://ror.org/05v7z3142","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210164614"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gong Jing","raw_affiliation_strings":["Jiangmen Polytechnic"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangmen Polytechnic","institution_ids":["https://openalex.org/I4210164614"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9976000189781189,"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":0.9976000189781189,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.0003000000142492354,"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":0.847000002861023},{"id":"https://openalex.org/keywords/permutation-feature-importance","display_name":"permutation importance","score":0.8090000152587891},{"id":"https://openalex.org/keywords/one-hot-encoding","display_name":"one-hot encoding","score":0.9829999804496765},{"id":"https://openalex.org/keywords/categorical-variables","display_name":"categorical variables","score":0.5099999904632568},{"id":"https://openalex.org/keywords/college-students","display_name":"university students","score":0.7900000214576721},{"id":"https://openalex.org/keywords/threshold-optimization","display_name":"threshold optimization","score":0.7580000162124634},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.5540000200271606}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6600000262260437},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6381999850273132},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5332000255584717},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4860999882221222},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.45190000534057617},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4092000126838684},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.4016999900341034},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3806000053882599},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3095000088214874},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.3089999854564667},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.3009999990463257},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2614000141620636},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2529999911785126},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2524000108242035},{"id":"https://openalex.org/C202632270","wikidata":"https://www.wikidata.org/wiki/Q7798106","display_name":"Threshold model","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aidas71914.2026.11712826","doi":"https://doi.org/10.1109/aidas71914.2026.11712826","is_oa":true,"landing_page_url":"https://doi.org/10.1109/aidas71914.2026.11712826","pdf_url":null,"source":{"id":"https://openalex.org/S7407086711","display_name":"International Conference on Artificial Intelligence and Data Sciences (AiDAS)","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 7th International Conference on Artificial Intelligence and Data Sciences (AiDAS)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1109/aidas71914.2026.11712826","doi":"https://doi.org/10.1109/aidas71914.2026.11712826","is_oa":true,"landing_page_url":"https://doi.org/10.1109/aidas71914.2026.11712826","pdf_url":null,"source":{"id":"https://openalex.org/S7407086711","display_name":"International Conference on Artificial Intelligence and Data Sciences (AiDAS)","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 7th International Conference on Artificial Intelligence and Data Sciences (AiDAS)","raw_type":"proceedings-article"},"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/W2954052001","https://openalex.org/W3121065372","https://openalex.org/W3202767876","https://openalex.org/W4285815691","https://openalex.org/W4313586371","https://openalex.org/W4315628873","https://openalex.org/W4319049461","https://openalex.org/W4379259062","https://openalex.org/W4387358492","https://openalex.org/W4389231539","https://openalex.org/W4390572162","https://openalex.org/W4390824079","https://openalex.org/W4391247782","https://openalex.org/W4391515181","https://openalex.org/W4398206401","https://openalex.org/W4399440180","https://openalex.org/W4401692097","https://openalex.org/W4407362880","https://openalex.org/W4414231523"],"related_works":["https://openalex.org/W3174515238","https://openalex.org/W7204175741","https://openalex.org/W7082635909","https://openalex.org/W2749232675","https://openalex.org/W2754662322","https://openalex.org/W6922571418","https://openalex.org/W6977488844","https://openalex.org/W6908955170","https://openalex.org/W2235694506","https://openalex.org/W7009641389"],"abstract_inverted_index":{"As":[0],"competition":[1],"intensifies":[2],"in":[3],"an":[4,134],"increasingly":[5],"saturated":[6],"mobile":[7],"market,":[8],"accurately":[9],"predicting":[10],"churn":[11],"among":[12],"university":[13],"students":[14],"with":[15],"distinctive":[16],"usage":[17],"and":[18,36,51,75,108,177],"switching":[19],"behaviors":[20],"is":[21,139],"important":[22],"for":[23,78],"effective":[24],"retention":[25],"strategies.":[26],"However,":[27],"survey-based":[28],"student":[29,157],"datasets":[30],"often":[31],"contain":[32],"high-cardinality":[33],"categorical":[34],"fields,":[35],"one-hot":[37,80,97,116,131],"encoding":[38],"expands":[39],"them":[40],"into":[41],"a":[42,71,91,110,129,155],"high-dimensional":[43],"sparse":[44,79],"space":[45],"that":[46,161],"may":[47],"introduce":[48],"redundant":[49],"indicators":[50],"increase":[52],"computational":[53],"overhead.":[54],"Building":[55],"on":[56,60,94,141,154],"our":[57],"earlier":[58],"work":[59],"importance-driven":[61],"feature":[62,120],"selection,":[63],"this":[64],"article":[65],"refines":[66],"the":[67,87,95,105,162,182],"pipeline":[68],"to":[69,145],"provide":[70],"more":[72],"compact,":[73],"efficient,":[74],"deployment-friendly":[76],"solution":[77],"survey":[81,158],"data.":[82],"Within":[83],"each":[84],"cross-validation":[85,153],"fold,":[86],"proposed":[88,163],"workflow":[89,166],"trains":[90],"Random":[92],"Forest":[93],"full":[96],"training":[98,106],"space,":[99],"computes":[100],"permutation":[101,165],"importance":[102],"using":[103,150],"only":[104],"data,":[107],"retains":[109],"controllable":[111],"Top-K":[112,164],"set":[113],"of":[114,184],"influential":[115],"indicators.":[117,186],"The":[118],"selected":[119,185],"subsets":[121],"are":[122],"evaluated":[123],"through":[124],"direct":[125],"column-subspace":[126],"selection":[127],"from":[128],"fixed":[130],"matrix,":[132],"while":[133,171],"optional":[135],"lightweight":[136],"threshold":[137],"search":[138],"performed":[140],"training-set":[142],"predicted":[143],"probabilities":[144],"support":[146],"decision-oriented":[147],"metrics.":[148],"Experiments":[149],"stratified":[151],"five-fold":[152],"customized":[156],"dataset":[159],"show":[160],"maintains":[167],"competitive":[168],"predictive":[169],"performance":[170],"substantially":[172],"reducing":[173],"feature-subspace":[174],"evaluation":[175],"time":[176],"providing":[178],"explicit":[179],"control":[180],"over":[181],"number":[183]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-10-07T00:00:00"}