{"id":"https://openalex.org/W4405036835","doi":"https://doi.org/10.1145/3706468.3706485","title":"Evaluating the Impact of Data Augmentation on Predictive Model Performance","display_name":"Evaluating the Impact of Data Augmentation on Predictive Model Performance","publication_year":2025,"publication_date":"2025-02-21","ids":{"openalex":"https://openalex.org/W4405036835","doi":"https://doi.org/10.1145/3706468.3706485"},"language":"en","primary_location":{"id":"doi:10.1145/3706468.3706485","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3706468.3706485","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 15th International Learning Analytics and Knowledge Conference","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3706468.3706485","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060307530","display_name":"Valdemar \u0160v\u00e1bensk\u00fd","orcid":"https://orcid.org/0000-0001-8546-280X"},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Valdemar \u0160v\u00e1bensk\u00fd","raw_affiliation_strings":["Kyushu University, Fukuoka, Japan"],"raw_orcid":"https://orcid.org/0000-0001-8546-280X","affiliations":[{"raw_affiliation_string":"Kyushu University, Fukuoka, Japan","institution_ids":["https://openalex.org/I135598925"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037442366","display_name":"Conrad Borchers","orcid":"https://orcid.org/0000-0003-3437-8979"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Conrad Borchers","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"raw_orcid":"https://orcid.org/0000-0003-3437-8979","affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035033051","display_name":"Elizabeth B. Cloude","orcid":"https://orcid.org/0000-0002-7599-6768"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Elizabeth B. Cloude","raw_affiliation_strings":["Michigan State University, Lansing, MI, USA"],"raw_orcid":"https://orcid.org/0000-0002-7599-6768","affiliations":[{"raw_affiliation_string":"Michigan State University, Lansing, MI, USA","institution_ids":["https://openalex.org/I87216513"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046057343","display_name":"Atsushi Shimada","orcid":"https://orcid.org/0000-0002-3635-9336"},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Atsushi Shimada","raw_affiliation_strings":["Kyushu University, Fukuoka, Japan"],"raw_orcid":"https://orcid.org/0000-0002-3635-9336","affiliations":[{"raw_affiliation_string":"Kyushu University, Fukuoka, Japan","institution_ids":["https://openalex.org/I135598925"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"126","last_page":"136"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9991000294685364,"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":0.9991000294685364,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9957000017166138,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9944999814033508,"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/computer-science","display_name":"Computer science","score":0.7535071969032288},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.6720982193946838},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6534131169319153},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5632582902908325},{"id":"https://openalex.org/keywords/chaining","display_name":"Chaining","score":0.5491238236427307},{"id":"https://openalex.org/keywords/performance-prediction","display_name":"Performance prediction","score":0.43460237979888916},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4313224256038666},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4311799705028534},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4154511094093323},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.09103304147720337}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7535071969032288},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6720982193946838},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6534131169319153},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5632582902908325},{"id":"https://openalex.org/C49020025","wikidata":"https://www.wikidata.org/wiki/Q1059099","display_name":"Chaining","level":2,"score":0.5491238236427307},{"id":"https://openalex.org/C2777115002","wikidata":"https://www.wikidata.org/wiki/Q7168246","display_name":"Performance prediction","level":2,"score":0.43460237979888916},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4313224256038666},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4311799705028534},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4154511094093323},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.09103304147720337},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3706468.3706485","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3706468.3706485","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 15th International Learning Analytics and Knowledge Conference","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2412.02108","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2412.02108","pdf_url":"https://arxiv.org/pdf/2412.02108","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3706468.3706485","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3706468.3706485","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 15th International Learning Analytics and Knowledge Conference","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2358816916","display_name":null,"funder_award_id":"JPMJCR22D1","funder_id":"https://openalex.org/F4320338075","funder_display_name":"Core Research for Evolutional Science and Technology"},{"id":"https://openalex.org/G6718509927","display_name":null,"funder_award_id":"CREST","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G7160405064","display_name":null,"funder_award_id":"JPMXP0518071489","funder_id":"https://openalex.org/F4320320912","funder_display_name":"Ministry of Education, Culture, Sports, Science and Technology"},{"id":"https://openalex.org/G7548823080","display_name":null,"funder_award_id":"JP22H00551","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G7879866177","display_name":null,"funder_award_id":"CREST","funder_id":"https://openalex.org/F4320320912","funder_display_name":"Ministry of Education, Culture, Sports, Science and Technology"}],"funders":[{"id":"https://openalex.org/F4320320912","display_name":"Ministry of Education, Culture, Sports, Science and Technology","ror":"https://ror.org/048rj2z13"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"},{"id":"https://openalex.org/F4320338075","display_name":"Core Research for Evolutional Science and Technology","ror":"https://ror.org/00097mb19"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W973843712","https://openalex.org/W1569653074","https://openalex.org/W1995864528","https://openalex.org/W2003759405","https://openalex.org/W2042571564","https://openalex.org/W2058064543","https://openalex.org/W2072035149","https://openalex.org/W2095742734","https://openalex.org/W2104933073","https://openalex.org/W2105043933","https://openalex.org/W2110065044","https://openalex.org/W2148143831","https://openalex.org/W2157825442","https://openalex.org/W2273614765","https://openalex.org/W2328176404","https://openalex.org/W2341519927","https://openalex.org/W2592799768","https://openalex.org/W2611176525","https://openalex.org/W2615786590","https://openalex.org/W2787225861","https://openalex.org/W2889366067","https://openalex.org/W2902242083","https://openalex.org/W2945063286","https://openalex.org/W2954996726","https://openalex.org/W2955609745","https://openalex.org/W2971296908","https://openalex.org/W3029578895","https://openalex.org/W3036567070","https://openalex.org/W3040135649","https://openalex.org/W3085763522","https://openalex.org/W3086358590","https://openalex.org/W3169504656","https://openalex.org/W3180181113","https://openalex.org/W3190520168","https://openalex.org/W3209200483","https://openalex.org/W4214922946","https://openalex.org/W4221074700","https://openalex.org/W4226323522","https://openalex.org/W4245830938","https://openalex.org/W4293004830","https://openalex.org/W4309028162","https://openalex.org/W4309048043","https://openalex.org/W4312211780","https://openalex.org/W4313065224","https://openalex.org/W4321441981","https://openalex.org/W4362700445","https://openalex.org/W4383181308","https://openalex.org/W4384927569","https://openalex.org/W4386321390","https://openalex.org/W4388497937","https://openalex.org/W4391457558","https://openalex.org/W4392445357","https://openalex.org/W4392445438","https://openalex.org/W4392445454","https://openalex.org/W4392483979","https://openalex.org/W4392484236","https://openalex.org/W4392484237","https://openalex.org/W4392484240","https://openalex.org/W4399583847","https://openalex.org/W4400438459","https://openalex.org/W4403970077","https://openalex.org/W6949186767","https://openalex.org/W6949379576"],"related_works":["https://openalex.org/W4390421286","https://openalex.org/W4280563792","https://openalex.org/W2140186469","https://openalex.org/W4389724018","https://openalex.org/W4318719684","https://openalex.org/W2775233965","https://openalex.org/W4318559728","https://openalex.org/W4360995913","https://openalex.org/W4312193868","https://openalex.org/W4308165509"],"abstract_inverted_index":{"In":[0,109],"supervised":[1],"machine":[2],"learning":[3,19],"(SML)":[4],"research,":[5],"large":[6],"training":[7,102],"datasets":[8],"are":[9,176],"essential":[10],"for":[11,170],"valid":[12],"results.":[13],"However,":[14],"obtaining":[15],"primary":[16],"data":[17,45],"in":[18,37,54],"analytics":[20],"(LA)":[21],"is":[22,64,153],"challenging.":[23],"Data":[24],"augmentation":[25,46,85,136],"can":[26],"address":[27],"this":[28],"by":[29,96],"expanding":[30],"and":[31,48,98,119,175],"diversifying":[32],"data,":[33],"though":[34],"its":[35],"use":[36],"LA":[38,57,171,190],"remains":[39],"underexplored.":[40],"This":[41,150],"paper":[42],"systematically":[43],"compares":[44],"techniques":[47,137,162],"their":[49],"impact":[50],"on":[51,66,80],"prediction":[52,59],"performance":[53,141,144,168],"a":[55,75,196],"typical":[56],"task:":[58],"of":[60,115,173],"academic":[61],"outcomes.":[62],"Augmentation":[63],"demonstrated":[65],"four":[67],"SML":[68],"models,":[69],"which":[70],"we":[71,111],"successfully":[72],"replicated":[73],"from":[74,194],"previous":[76],"LAK":[77],"study":[78,198],"based":[79],"AUC":[81,95],"values.":[82],"Among":[83],"21":[84,117],"techniques,":[86,118],"SMOTE-ENN":[87,132],"sampling":[88,161],"performed":[89],"the":[90,93,101,106,164,189],"best,":[91],"improving":[92],"average":[94],"0.01":[97],"approximately":[99],"halving":[100],"time":[103],"compared":[104,112],"to":[105,131,147],"baseline":[107],"models.":[108],"addition,":[110],"99":[113],"combinations":[114],"chaining":[116],"found":[120],"minor,":[121],"although":[122],"statistically":[123,166],"significant,":[124],"improvements":[125,169],"across":[126],"models":[127],"when":[128],"adding":[129],"noise":[130],"(+0.014).":[133],"Notably,":[134],"some":[135],"significantly":[138],"lowered":[139],"predictive":[140],"or":[142],"increased":[143],"fluctuation":[145],"related":[146],"random":[148],"chance.":[149],"paper's":[151],"contribution":[152],"twofold.":[154],"Primarily,":[155],"our":[156],"empirical":[157],"findings":[158],"show":[159],"that":[160],"provide":[163],"most":[165],"reliable":[167],"applications":[172],"SML,":[174],"computationally":[177],"more":[178],"efficient":[179],"than":[180],"deep":[181],"generation":[182],"methods":[183],"with":[184],"complex":[185],"hyperparameter":[186],"settings.":[187],"Second,":[188],"community":[191],"may":[192],"benefit":[193],"validating":[195],"recent":[197],"through":[199],"independent":[200],"replication.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2024-12-06T00:00:00"}