{"id":"https://openalex.org/W7139952681","doi":"https://doi.org/10.48550/arxiv.2603.18190","title":"Starting Off on the Wrong Foot: Pitfalls in Data Preparation","display_name":"Starting Off on the Wrong Foot: Pitfalls in Data Preparation","publication_year":2026,"publication_date":"2026-03-18","ids":{"openalex":"https://openalex.org/W7139952681","doi":"https://doi.org/10.48550/arxiv.2603.18190"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.18190","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18190","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.18190","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130244585","display_name":"Jiayi Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Jiayi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101309754","display_name":"Panyi Dong","orcid":"https://orcid.org/0000-0003-0957-0141"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Panyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5042065650","display_name":"Zhiyu Quan","orcid":"https://orcid.org/0000-0001-9735-6713"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Quan, Zhiyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.11580000072717667,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.11580000072717667,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.09049999713897705,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.08349999785423279,"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/interpretability","display_name":"Interpretability","score":0.7095000147819519},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5268999934196472},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4302999973297119},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.39739999175071716},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.3634999990463257},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.34869998693466187},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3465999960899353},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.2872999906539917}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7095000147819519},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6507999897003174},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5268999934196472},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5180000066757202},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4302999973297119},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.39739999175071716},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3634999990463257},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.34869998693466187},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3465999960899353},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3440999984741211},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3197000026702881},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.2928999960422516},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C92446256","wikidata":"https://www.wikidata.org/wiki/Q3306762","display_name":"Data validation","level":2,"score":0.2863999903202057},{"id":"https://openalex.org/C71008984","wikidata":"https://www.wikidata.org/wiki/Q2890076","display_name":"Rigour","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C93361087","wikidata":"https://www.wikidata.org/wiki/Q4426698","display_name":"Data consistency","level":2,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.18190","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18190","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.18190","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18190","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.6539691686630249}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"When":[0],"working":[1],"with":[2,47],"real-world":[3],"insurance":[4,50,171,189],"data,":[5],"practitioners":[6],"often":[7,39,137],"encounter":[8],"challenges":[9],"during":[10],"the":[11,18,82,126,140],"data":[12,31,61,73,151],"preparation":[13,32,62,152],"stage":[14],"that":[15,29,109,147],"can":[16],"undermine":[17],"statistical":[19,67],"validity":[20],"and":[21,42,81,95,114,135,160],"reliability":[22],"of":[23,125],"downstream":[24],"modeling.":[25],"This":[26,175],"study":[27],"illustrates":[28],"conventional":[30],"procedures":[33],"such":[34],"as":[35],"random":[36],"train-test":[37],"partitioning,":[38],"yield":[40],"unreliable":[41],"unstable":[43],"results":[44,185],"when":[45],"confronted":[46],"highly":[48],"imbalanced":[49],"loss":[51,172],"data.":[52],"To":[53],"mitigate":[54],"these":[55,101],"limitations,":[56],"we":[57],"propose":[58],"a":[59,105,178],"novel":[60],"framework":[63,108,117],"leveraging":[64],"two":[65],"recent":[66],"advancements:":[68],"support":[69],"points":[70],"for":[71,86,182],"representative":[72],"splitting":[74],"to":[75,91],"ensure":[76],"distributional":[77],"consistency":[78],"across":[79,169],"partitions,":[80],"Chatterjee":[83],"correlation":[84],"coefficient":[85],"initial,":[87],"non-parametric":[88],"feature":[89,93],"screening":[90],"capture":[92],"relevance":[94],"dependence":[96],"structure.":[97],"We":[98],"further":[99],"integrate":[100],"theoretical":[102],"advances":[103],"into":[104],"unified,":[106],"efficient":[107],"also":[110,163],"incorporates":[111],"missing-data":[112],"handling,":[113],"embed":[115],"this":[116],"within":[118],"our":[119],"custom":[120],"InsurAutoML":[121],"pipeline.":[122],"The":[123],"performance":[124],"proposed":[127],"approach":[128],"is":[129],"evaluated":[130],"using":[131],"both":[132],"simulated":[133],"datasets":[134,136],"cited":[138],"in":[139,186],"academic":[141],"literature.":[142],"Our":[143],"findings":[144],"definitively":[145],"demonstrate":[146],"incorporating":[148],"statistically":[149],"rigorous":[150],"methods":[153],"not":[154],"only":[155],"significantly":[156],"enhances":[157],"model":[158],"robustness":[159],"interpretability":[161],"but":[162],"substantially":[164],"reduces":[165],"computational":[166],"resource":[167],"requirements":[168],"diverse":[170],"modeling":[173],"tasks.":[174],"work":[176],"provides":[177],"crucial":[179],"methodological":[180],"upgrade":[181],"achieving":[183],"reliable":[184],"high":[187],"stakes":[188],"applications.":[190]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-21T00:00:00"}