{"id":"https://openalex.org/W4414928126","doi":"https://doi.org/10.1002/for.70026","title":"Multi\u2010Classifier Evidence Ensemble Algorithm\u2010Based for Predicting Travelers Repurchases of China's Airlines","display_name":"Multi\u2010Classifier Evidence Ensemble Algorithm\u2010Based for Predicting Travelers Repurchases of China's Airlines","publication_year":2025,"publication_date":"2025-10-08","ids":{"openalex":"https://openalex.org/W4414928126","doi":"https://doi.org/10.1002/for.70026"},"language":"en","primary_location":{"id":"doi:10.1002/for.70026","doi":"https://doi.org/10.1002/for.70026","is_oa":false,"landing_page_url":"https://doi.org/10.1002/for.70026","pdf_url":null,"source":{"id":"https://openalex.org/S122161766","display_name":"Journal of Forecasting","issn_l":"0277-6693","issn":["0277-6693","1099-131X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","fnege-3","jufo-1","ki-jl-1","norway-1","poland-70","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Forecasting","raw_type":"journal-article"},"volume":null,"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/A5086415643","display_name":"Yanhong Chen","orcid":"https://orcid.org/0000-0001-5632-1397","observed_orcids":["https://orcid.org/0000-0001-5632-1397"]},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanhong Chen","raw_affiliation_strings":["China Center for Special Economic Zone Research, Shenzhen University, and Shenzhen Municipal Committee of the Chinese People's Political Consultative Conference  Shenzhen Guangdong People's Republic of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Center for Special Economic Zone Research, Shenzhen University, and Shenzhen Municipal Committee of the Chinese People's Political Consultative Conference  Shenzhen Guangdong People's Republic of China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101677959","display_name":"Luning Liu","orcid":"https://orcid.org/0000-0002-5539-5623","observed_orcids":["https://orcid.org/0000-0002-5539-5623"]},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Luning Liu","raw_affiliation_strings":["National Research Center for Language Technology and Digital Economy  Harbin China","School of Management Harbin Institute of Technology  Harbin Heilongjiang People's Republic of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Research Center for Language Technology and Digital Economy  Harbin China","institution_ids":[]},{"raw_affiliation_string":"School of Management Harbin Institute of Technology  Harbin Heilongjiang People's Republic of China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000121999","display_name":"Dequan Zheng","orcid":"https://orcid.org/0000-0002-6431-360X","observed_orcids":["https://orcid.org/0000-0002-6431-360X"]},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dequan Zheng","raw_affiliation_strings":["School of Management Harbin Institute of Technology  Harbin Heilongjiang People's Republic of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Management Harbin Institute of Technology  Harbin Heilongjiang People's Republic of China","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5000121999","https://openalex.org/A5101677959"],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":{"value":3400,"currency":"USD","value_usd":3400},"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20311474,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"45","issue":"1","first_page":"260","last_page":"271"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9452999830245972,"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":0.9452999830245972,"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":0.015300000086426735,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.008299999870359898,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/air-passengers","display_name":"airline passengers","score":0.8429999947547913},{"id":"https://openalex.org/keywords/dempster-shafer-evidence-theory","display_name":"Dempster-Shafer theory","score":0.7929999828338623},{"id":"https://openalex.org/keywords/multi-classifier-ensemble","display_name":"multi-classifier ensemble","score":0.9259999990463257},{"id":"https://openalex.org/keywords/passenger-behavior","display_name":"passenger behavior","score":0.5550000071525574},{"id":"https://openalex.org/keywords/low-cost-carriers","display_name":"low-cost carriers","score":0.7570000290870667},{"id":"https://openalex.org/keywords/customer-loyalty","display_name":"customer loyalty","score":0.5049999952316284},{"id":"https://openalex.org/keywords/behavioral-characteristics","display_name":"behavioral characteristics","score":0.6000000238418579}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7358999848365784},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6869000196456909},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5354999899864197},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5291000008583069},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5271999835968018},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4999000132083893},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.4973999857902527},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4724000096321106},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.44679999351501465},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.3952000141143799},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.38510000705718994},{"id":"https://openalex.org/C166052673","wikidata":"https://www.wikidata.org/wiki/Q83021","display_name":"Empirical evidence","level":2,"score":0.38019999861717224},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37860000133514404},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.314300000667572},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3027999997138977},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.29429998993873596},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.2775000035762787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1002/for.70026","doi":"https://doi.org/10.1002/for.70026","is_oa":false,"landing_page_url":"https://doi.org/10.1002/for.70026","pdf_url":null,"source":{"id":"https://openalex.org/S122161766","display_name":"Journal of Forecasting","issn_l":"0277-6693","issn":["0277-6693","1099-131X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","fnege-3","jufo-1","ki-jl-1","norway-1","poland-70","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Forecasting","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G2408270191","display_name":null,"funder_award_id":"#71974044","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2699012874","display_name":null,"funder_award_id":"#72034001","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W28412257","https://openalex.org/W1494223030","https://openalex.org/W1594730842","https://openalex.org/W1689771227","https://openalex.org/W1976450565","https://openalex.org/W2002808981","https://openalex.org/W2005791035","https://openalex.org/W2022122945","https://openalex.org/W2028809046","https://openalex.org/W2039316620","https://openalex.org/W2077575809","https://openalex.org/W2093717447","https://openalex.org/W2095484565","https://openalex.org/W2103369349","https://openalex.org/W2117079761","https://openalex.org/W2135870748","https://openalex.org/W2150249378","https://openalex.org/W2364148092","https://openalex.org/W2417999172","https://openalex.org/W2498672755","https://openalex.org/W2530334853","https://openalex.org/W2581423799","https://openalex.org/W2618999993","https://openalex.org/W2709480298","https://openalex.org/W2753546593","https://openalex.org/W2773695206","https://openalex.org/W2779704869","https://openalex.org/W2786141192","https://openalex.org/W2789758093","https://openalex.org/W2912934387","https://openalex.org/W2923371295","https://openalex.org/W2928473576","https://openalex.org/W2950877942","https://openalex.org/W2953034770","https://openalex.org/W2970602317","https://openalex.org/W2971396385","https://openalex.org/W2997226100","https://openalex.org/W3015159462","https://openalex.org/W3020889265","https://openalex.org/W3022700411","https://openalex.org/W3026410712","https://openalex.org/W3048352145","https://openalex.org/W3082515978","https://openalex.org/W3092065965","https://openalex.org/W3092168368","https://openalex.org/W3095806913","https://openalex.org/W3108746168","https://openalex.org/W3109439621","https://openalex.org/W3117837329","https://openalex.org/W3125204069","https://openalex.org/W3129637043","https://openalex.org/W3131530676","https://openalex.org/W3156845052","https://openalex.org/W3159413902","https://openalex.org/W3193354972","https://openalex.org/W3202800081","https://openalex.org/W3216066124","https://openalex.org/W4213203885","https://openalex.org/W4214513907","https://openalex.org/W4291712388","https://openalex.org/W4293167886","https://openalex.org/W4301347335","https://openalex.org/W4308112201","https://openalex.org/W4392187129","https://openalex.org/W4401752434","https://openalex.org/W4405601261","https://openalex.org/W4405654893"],"related_works":[],"abstract_inverted_index":{"ABSTRACT":[0],"Repurchase":[1],"prediction":[2,134],"is":[3],"a":[4,11,33,61],"vital":[5],"aspect":[6],"of":[7,77,121,138],"marketing":[8],"strategy":[9],"and":[10,25,86,95,113,119],"complex":[12],"decision\u2010making":[13],"task,":[14],"especially":[15],"in":[16,75,97,136,147],"the":[17,69,78,80,82,84,87,117,122,128],"airline":[18,144],"industry,":[19],"where":[20],"data":[21],"are":[22],"uncertain,":[23],"incomplete,":[24],"ambiguous.":[26],"To":[27],"address":[28],"this,":[29],"this":[30],"study":[31],"proposes":[32],"novel":[34],"multi\u2010classifier":[35],"evidence":[36,41],"ensemble":[37,130],"algorithm":[38,71],"that":[39,68,127],"integrates":[40],"theory":[42],"with":[43],"machine":[44],"learning":[45],"to":[46],"predict":[47],"travelers'":[48],"repurchase":[49],"behavior.":[50],"The":[51,124],"model":[52],"was":[53],"trained":[54],"using":[55,103],"29":[56],"behavioral":[57],"features":[58],"derived":[59],"from":[60],"low\u2010cost":[62],"Chinese":[63],"airline.":[64],"Empirical":[65],"results":[66,125],"show":[67],"proposed":[70,129],"outperforms":[72,132],"traditional":[73,133],"models":[74,135],"terms":[76,137],"accuracy,":[79],"precision,":[81],"recall,":[83],"F1\u2010score,":[85],"AUC.":[88],"Specifically,":[89],"it":[90],"achieved":[91],"over":[92],"80%":[93],"accuracy":[94],"precision":[96],"binary":[98],"classification":[99],"tasks.":[100],"Ablation":[101],"experiments":[102],"four":[104],"classifier":[105],"combinations":[106],"at":[107],"different":[108],"sampling":[109],"rates":[110],"(30%,":[111],"50%,":[112],"70%)":[114],"further":[115],"validated":[116],"robustness":[118],"effectiveness":[120],"framework.":[123],"suggest":[126],"framework":[131],"overall":[139],"predictive":[140],"performance":[141],"for":[142],"analyzing":[143],"passenger":[145],"behavior":[146],"real\u2010world":[148],"settings.":[149]},"counts_by_year":[],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2025-10-10T00:00:00"}