{"id":"https://openalex.org/W4402983618","doi":"https://doi.org/10.3390/e26100835","title":"Information FOMO: The Unhealthy Fear of Missing Out on Information\u2014A Method for Removing Misleading Data for Healthier Models","display_name":"Information FOMO: The Unhealthy Fear of Missing Out on Information\u2014A Method for Removing Misleading Data for Healthier Models","publication_year":2024,"publication_date":"2024-09-30","ids":{"openalex":"https://openalex.org/W4402983618","doi":"https://doi.org/10.3390/e26100835","pmid":"https://pubmed.ncbi.nlm.nih.gov/39451911"},"language":"en","primary_location":{"id":"doi:10.3390/e26100835","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e26100835","pdf_url":"https://www.mdpi.com/1099-4300/26/10/835/pdf?version=1728961877","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/26/10/835/pdf?version=1728961877","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061968827","display_name":"Ethan Pickering","orcid":"https://orcid.org/0000-0002-4485-6359"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ethan Pickering","raw_affiliation_strings":["Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA"],"raw_orcid":"https://orcid.org/0000-0002-4485-6359","affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037328807","display_name":"Themistoklis P. Sapsis","orcid":"https://orcid.org/0000-0003-0302-0691"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Themistoklis P. Sapsis","raw_affiliation_strings":["Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA"],"raw_orcid":"https://orcid.org/0000-0003-0302-0691","affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA","institution_ids":["https://openalex.org/I63966007"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5037328807","https://openalex.org/A5061968827"],"corresponding_institution_ids":["https://openalex.org/I63966007"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":0.6508,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.75114839,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":"26","issue":"10","first_page":"835","last_page":"835"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9998000264167786,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9998000264167786,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9902999997138977,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T11236","display_name":"Control Systems and Identification","score":0.9843999743461609,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7476055026054382},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.5663478374481201},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5575684309005737},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5282135605812073},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5052648186683655},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4941911995410919},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.48743465542793274},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4767487943172455},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.47649115324020386},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4642636775970459},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.453108012676239},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.44820478558540344},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4449029564857483},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.43932706117630005},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.23202967643737793},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.20899394154548645},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13192883133888245}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7476055026054382},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.5663478374481201},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5575684309005737},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5282135605812073},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5052648186683655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4941911995410919},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.48743465542793274},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4767487943172455},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.47649115324020386},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4642636775970459},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.453108012676239},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.44820478558540344},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4449029564857483},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.43932706117630005},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.23202967643737793},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.20899394154548645},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13192883133888245},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","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},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/e26100835","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e26100835","pdf_url":"https://www.mdpi.com/1099-4300/26/10/835/pdf?version=1728961877","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmid:39451911","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/39451911","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:11507899","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11507899","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11507899/pdf/entropy-26-00835.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"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":"Entropy (Basel)","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:7a321d9b78754446970c175f43783ee9","is_oa":false,"landing_page_url":"https://doaj.org/article/7a321d9b78754446970c175f43783ee9","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 26, Iss 10, p 835 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/e26100835","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e26100835","pdf_url":"https://www.mdpi.com/1099-4300/26/10/835/pdf?version=1728961877","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.550000011920929,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G1523888516","display_name":null,"funder_award_id":"FA9550-","funder_id":"https://openalex.org/F4320338279","funder_display_name":"Air Force Office of Scientific Research"},{"id":"https://openalex.org/G4516736450","display_name":null,"funder_award_id":"-21-1-","funder_id":"https://openalex.org/F4320338279","funder_display_name":"Air Force Office of Scientific Research"},{"id":"https://openalex.org/G5809100787","display_name":null,"funder_award_id":"FA9550","funder_id":"https://openalex.org/F4320338279","funder_display_name":"Air Force Office of Scientific Research"},{"id":"https://openalex.org/G8305032408","display_name":null,"funder_award_id":"FA9550-23-1-0517","funder_id":"https://openalex.org/F4320338279","funder_display_name":"Air Force Office of Scientific Research"},{"id":"https://openalex.org/G8709796777","display_name":null,"funder_award_id":"FA9550-21-1-0058","funder_id":"https://openalex.org/F4320338279","funder_display_name":"Air Force Office of Scientific Research"}],"funders":[{"id":"https://openalex.org/F4320333591","display_name":"Multidisciplinary University Research Initiative","ror":null},{"id":"https://openalex.org/F4320338279","display_name":"Air Force Office of Scientific Research","ror":"https://ror.org/011e9bt93"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4402983618.pdf","grobid_xml":"https://content.openalex.org/works/W4402983618.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W1502922572","https://openalex.org/W1962364105","https://openalex.org/W1978272081","https://openalex.org/W2034978228","https://openalex.org/W2037241331","https://openalex.org/W2061079066","https://openalex.org/W2062456242","https://openalex.org/W2069761125","https://openalex.org/W2070902649","https://openalex.org/W2125993116","https://openalex.org/W2133055236","https://openalex.org/W2162287622","https://openalex.org/W2192203593","https://openalex.org/W2336104608","https://openalex.org/W2415726964","https://openalex.org/W2787894218","https://openalex.org/W2896549049","https://openalex.org/W2899283552","https://openalex.org/W2923764619","https://openalex.org/W2944619585","https://openalex.org/W2952594493","https://openalex.org/W2959995783","https://openalex.org/W2961792079","https://openalex.org/W2963115772","https://openalex.org/W2963518130","https://openalex.org/W2979786244","https://openalex.org/W3008768523","https://openalex.org/W3046184314","https://openalex.org/W3083720136","https://openalex.org/W3163205789","https://openalex.org/W3172995164","https://openalex.org/W4206410067","https://openalex.org/W4230674625","https://openalex.org/W4283205594","https://openalex.org/W4313430604"],"related_works":["https://openalex.org/W2959160600","https://openalex.org/W2142113611","https://openalex.org/W2119956050","https://openalex.org/W2056065776","https://openalex.org/W4324301570","https://openalex.org/W2045629210","https://openalex.org/W2017927971","https://openalex.org/W1708141795","https://openalex.org/W3029533605","https://openalex.org/W2115519811"],"abstract_inverted_index":{"Misleading":[0],"or":[1,11,45,188],"unnecessary":[2,47],"data":[3,40,66,155],"can":[4],"have":[5],"out-sized":[6],"impacts":[7],"on":[8,126],"the":[9,50,74,90,93,112,117,131,147,150,154,163,176],"health":[10],"accuracy":[12],"of":[13,53,73,89,92,146,175],"Machine":[14],"Learning":[15],"(ML)":[16],"models.":[17,204],"We":[18,82],"present":[19],"a":[20,36,87,143],"Bayesian":[21,27],"sequential":[22],"selection":[23,113,164],"method,":[24],"akin":[25],"to":[26,49,68,99],"experimental":[28],"design,":[29],"that":[30,41,180],"identifies":[31],"critically":[32],"important":[33],"information":[34,182],"within":[35],"dataset":[37],"while":[38],"ignoring":[39],"are":[42,86,97],"either":[43],"misleading":[44],"bring":[46],"complexity":[48,91],"surrogate":[51,75,203],"model":[52,119],"choice.":[54],"Our":[55,105],"method":[56,148,191],"improves":[57],"sample-wise":[58,79],"error":[59,171],"convergence":[60,145],"and":[61,71,96,102,120,159,169,199],"eliminates":[62],"instances":[63],"where":[64],"more":[65],"lead":[67],"worse":[69],"performance":[70],"instabilities":[72,85],"model,":[76,133],"often":[77],"termed":[78],"\"double":[80],"descent\".":[81],"find":[83],"these":[84],"result":[88],"underlying":[94],"map":[95],"linked":[98],"extreme":[100],"events":[101],"heavy":[103],"tails.":[104],"approach":[106],"has":[107],"two":[108],"key":[109,181],"features.":[110],"First,":[111],"algorithm":[114],"dynamically":[115],"couples":[116],"chosen":[118,124],"data.":[121,141],"Data":[122],"is":[123,183,192],"based":[125],"its":[127],"merits":[128],"towards":[129],"improving":[130],"selected":[132],"rather":[134],"than":[135],"being":[136],"compared":[137],"strictly":[138],"against":[139],"other":[140],"Second,":[142],"natural":[144],"removes":[149],"need":[151],"for":[152],"dividing":[153],"into":[156],"training,":[157],"testing,":[158],"validation":[160,170],"sets.":[161],"Instead,":[162],"metric":[165],"inherently":[166],"assesses":[167],"testing":[168,187],"through":[172],"global":[173],"statistics":[174],"model.":[177],"This":[178],"ensures":[179],"never":[184],"wasted":[185],"in":[186],"validation.":[189],"The":[190],"applied":[193],"using":[194],"both":[195],"Gaussian":[196],"process":[197],"regression":[198],"deep":[200],"neural":[201],"network":[202]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}