{"id":"https://openalex.org/W4399418695","doi":"https://doi.org/10.1145/3651671.3651764","title":"Direct vs. Cross-Validated Stacking in Ensemble Learning: Evaluating the Trade-Off between Inference Time and Generalizability on Fashion-MNIST","display_name":"Direct vs. Cross-Validated Stacking in Ensemble Learning: Evaluating the Trade-Off between Inference Time and Generalizability on Fashion-MNIST","publication_year":2024,"publication_date":"2024-02-02","ids":{"openalex":"https://openalex.org/W4399418695","doi":"https://doi.org/10.1145/3651671.3651764"},"language":"en","primary_location":{"id":"doi:10.1145/3651671.3651764","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3651671.3651764","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 16th 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/A5033376720","display_name":"Raad Bin Tareaf","orcid":"https://orcid.org/0000-0002-2804-3243"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Raad Bin Tareaf","raw_affiliation_strings":["XU Exponential University of Applied Sciences, XU Exponential University of Applied Sciences, Germany"],"raw_orcid":"https://orcid.org/0000-0002-2804-3243","affiliations":[{"raw_affiliation_string":"XU Exponential University of Applied Sciences, XU Exponential University of Applied Sciences, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5099043665","display_name":"Alex Maximilian Korga","orcid":"https://orcid.org/0009-0000-6501-9309"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alex Maximilian Korga","raw_affiliation_strings":["XU Exponential University of Applied Sciences, XU Exponential University of Applied Sciences, Germany"],"raw_orcid":"https://orcid.org/0009-0000-6501-9309","affiliations":[{"raw_affiliation_string":"XU Exponential University of Applied Sciences, XU Exponential University of Applied Sciences, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039386514","display_name":"Sebastian Wefers","orcid":"https://orcid.org/0009-0005-4467-2786"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sebastian Wefers","raw_affiliation_strings":["XU Exponential University of Applied Sciences, XU Exponential University of Applied Sciences, Germany"],"raw_orcid":"https://orcid.org/0009-0005-4467-2786","affiliations":[{"raw_affiliation_string":"XU Exponential University of Applied Sciences, XU Exponential University of Applied Sciences, Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5099043667","display_name":"Keno Hanken","orcid":"https://orcid.org/0009-0004-9633-1660"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Keno Hanken","raw_affiliation_strings":["XU Exponential University of Applied Sciences, XU Exponential University of Applied Sciences, Germany"],"raw_orcid":"https://orcid.org/0009-0004-9633-1660","affiliations":[{"raw_affiliation_string":"XU Exponential University of Applied Sciences, XU Exponential University of Applied Sciences, Germany","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5033376720"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6557,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.85826176,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"453","last_page":"461"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9970999956130981,"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"}},"topics":[{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9970999956130981,"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/T10320","display_name":"Neural Networks and Applications","score":0.9944000244140625,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9930999875068665,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.8790132999420166},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.8623250722885132},{"id":"https://openalex.org/keywords/stacking","display_name":"Stacking","score":0.8460147976875305},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.721367597579956},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7155359983444214},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.664611279964447},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6523902416229248},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5654107332229614},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4220760762691498},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3474882245063782},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10833236575126648},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09871840476989746}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.8790132999420166},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.8623250722885132},{"id":"https://openalex.org/C33347731","wikidata":"https://www.wikidata.org/wiki/Q285210","display_name":"Stacking","level":2,"score":0.8460147976875305},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.721367597579956},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7155359983444214},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.664611279964447},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6523902416229248},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5654107332229614},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4220760762691498},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3474882245063782},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10833236575126648},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09871840476989746},{"id":"https://openalex.org/C46141821","wikidata":"https://www.wikidata.org/wiki/Q209402","display_name":"Nuclear magnetic resonance","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3651671.3651764","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3651671.3651764","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 16th International Conference on Machine Learning and Computing","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":7,"referenced_works":["https://openalex.org/W1589522534","https://openalex.org/W2981581709","https://openalex.org/W3015083507","https://openalex.org/W3033405082","https://openalex.org/W3100355572","https://openalex.org/W3111307227","https://openalex.org/W4248083651"],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W2886711096","https://openalex.org/W2750384547","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W4389249638","https://openalex.org/W2733410219","https://openalex.org/W4382468411","https://openalex.org/W4318751837"],"abstract_inverted_index":{"This":[0,142],"study":[1,101],"undertakes":[2],"a":[3],"detailed":[4],"evaluation":[5],"of":[6,47,81,106,153,163,192],"two":[7],"prominent":[8],"ensemble":[9,135,155],"learning":[10,50,130],"techniques":[11,136,156],"\u2013":[12,18],"direct":[13,89],"stacking":[14,17,64,71,110,174],"and":[15,33,57,76,85,160],"cross-validated":[16,70],"using":[19],"the":[20,28,45,63,79,104,107,151,161,173,181],"Fashion-MNIST":[21],"dataset.":[22],"The":[23,100],"focus":[24],"is":[25,95],"on":[26,138,171],"exploring":[27],"trade-offs":[29],"between":[30],"generalizability,":[31],"accuracy,":[32],"inference":[34,86],"time":[35],"inherent":[36],"in":[37,98,109,132,190],"these":[38,154],"methodologies.":[39],"Through":[40],"comprehensive":[41],"experimentation,":[42],"we":[43],"compare":[44],"performance":[46],"various":[48,158],"machine":[49,129],"models,":[51,111,185],"including":[52],"LightGBM,":[53],"XGBoost,":[54],"Logistic":[55],"Regression,":[56],"different":[58],"neural":[59],"network":[60],"architectures,":[61],"within":[62],"frameworks.":[65],"Our":[66,123,167],"results":[67,168],"reveal":[68],"that":[69,113,170],"typically":[72],"offers":[73],"superior":[74],"accuracy":[75,191],"generalizability":[77],"at":[78],"expense":[80],"increased":[82,114],"computational":[83],"complexity":[84,115],"time,":[87],"whereas":[88],"stacking,":[90],"though":[91],"computationally":[92],"more":[93],"efficient,":[94],"less":[96],"robust":[97],"performance.":[99,122],"also":[102,144],"examines":[103],"importance":[105],"architecture":[108],"emphasizing":[112],"does":[116],"not":[117],"necessarily":[118],"lead":[119],"to":[120],"enhanced":[121],"findings":[124],"provide":[125],"essential":[126],"insights":[127],"for":[128,147],"practitioners":[131],"selecting":[133],"suitable":[134],"based":[137],"their":[139],"specific":[140],"needs.":[141],"research":[143],"opens":[145],"avenues":[146],"future":[148],"investigations":[149],"into":[150],"application":[152],"across":[157],"domains":[159],"integration":[162],"advanced":[164],"model":[165],"architectures.":[166],"show":[169],"average":[172,188],"models":[175],"took":[176],"four":[177],"times":[178],"longer":[179],"than":[180],"respective":[182],"best":[183],"base":[184],"with":[186],"an":[187],"increase":[189],"0.875%.":[193]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}