{"id":"https://openalex.org/W4410397811","doi":"https://doi.org/10.32473/flairs.38.1.139000","title":"Kernel Density Estimated Linear Regression","display_name":"Kernel Density Estimated Linear Regression","publication_year":2025,"publication_date":"2025-05-14","ids":{"openalex":"https://openalex.org/W4410397811","doi":"https://doi.org/10.32473/flairs.38.1.139000"},"language":"en","primary_location":{"id":"doi:10.32473/flairs.38.1.139000","is_oa":true,"landing_page_url":"https://doi.org/10.32473/flairs.38.1.139000","pdf_url":"https://journals.flvc.org/FLAIRS/article/download/139000/144091","source":{"id":"https://openalex.org/S4210205383","display_name":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","issn_l":"2334-0754","issn":["2334-0754","2334-0762"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":"https://openalex.org/P4310320363","host_organization_name":"George A. Smathers Libraries","host_organization_lineage":["https://openalex.org/P4310320363"],"host_organization_lineage_names":["George A. Smathers Libraries"],"type":"conference"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The International FLAIRS Conference Proceedings","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://journals.flvc.org/FLAIRS/article/download/139000/144091","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5117555422","display_name":"Roshan Kalpavruksha","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Roshan Kalpavruksha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117555423","display_name":"Rohan Kalpavruksha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rohan Kalpavruksha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086605001","display_name":"Teryn Cha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Teryn Cha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5108635803","display_name":"Sung-Hyuk Cha","orcid":"https://orcid.org/0009-0007-8116-681X"},"institutions":[{"id":"https://openalex.org/I126863827","display_name":"Pace University","ror":"https://ror.org/047p7y759","country_code":"US","type":"education","lineage":["https://openalex.org/I126863827"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sung-Hyuk Cha","raw_affiliation_strings":["Pace University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pace University","institution_ids":["https://openalex.org/I126863827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5117555422"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.27050184,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"38","issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.6460999846458435,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.6460999846458435,"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/statistics","display_name":"Statistics","score":0.5979087352752686},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.5272789597511292},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5040522813796997},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.48759809136390686},{"id":"https://openalex.org/keywords/kernel-regression","display_name":"Kernel regression","score":0.4743415415287018},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.44158363342285156},{"id":"https://openalex.org/keywords/principal-component-regression","display_name":"Principal component regression","score":0.4236010015010834},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.07910045981407166}],"concepts":[{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5979087352752686},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.5272789597511292},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5040522813796997},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.48759809136390686},{"id":"https://openalex.org/C200695384","wikidata":"https://www.wikidata.org/wiki/Q1739319","display_name":"Kernel regression","level":3,"score":0.4743415415287018},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.44158363342285156},{"id":"https://openalex.org/C74887250","wikidata":"https://www.wikidata.org/wiki/Q3455892","display_name":"Principal component regression","level":3,"score":0.4236010015010834},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.07910045981407166}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.32473/flairs.38.1.139000","is_oa":true,"landing_page_url":"https://doi.org/10.32473/flairs.38.1.139000","pdf_url":"https://journals.flvc.org/FLAIRS/article/download/139000/144091","source":{"id":"https://openalex.org/S4210205383","display_name":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","issn_l":"2334-0754","issn":["2334-0754","2334-0762"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":"https://openalex.org/P4310320363","host_organization_name":"George A. Smathers Libraries","host_organization_lineage":["https://openalex.org/P4310320363"],"host_organization_lineage_names":["George A. Smathers Libraries"],"type":"conference"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The International FLAIRS Conference Proceedings","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:81f519a2a81f48ff9ae169d5f2cc94b7","is_oa":true,"landing_page_url":"https://doaj.org/article/81f519a2a81f48ff9ae169d5f2cc94b7","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proceedings of the International Florida Artificial Intelligence Research Society Conference, Vol 38, Iss 1 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.32473/flairs.38.1.139000","is_oa":true,"landing_page_url":"https://doi.org/10.32473/flairs.38.1.139000","pdf_url":"https://journals.flvc.org/FLAIRS/article/download/139000/144091","source":{"id":"https://openalex.org/S4210205383","display_name":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","issn_l":"2334-0754","issn":["2334-0754","2334-0762"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":"https://openalex.org/P4310320363","host_organization_name":"George A. Smathers Libraries","host_organization_lineage":["https://openalex.org/P4310320363"],"host_organization_lineage_names":["George A. Smathers Libraries"],"type":"conference"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The International FLAIRS Conference Proceedings","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410397811.pdf","grobid_xml":"https://content.openalex.org/works/W4410397811.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2213164457","https://openalex.org/W2799728137","https://openalex.org/W31220157","https://openalex.org/W2804477311","https://openalex.org/W3114452526","https://openalex.org/W2995837313","https://openalex.org/W2363930281","https://openalex.org/W3093872600","https://openalex.org/W2365382492","https://openalex.org/W2159399953"],"abstract_inverted_index":{"Regression":[0,93],"analysis":[1],"is":[2,30],"a":[3,76,223,230,252],"cornerstone":[4],"of":[5,17,115,147,152,183,186,233],"predictive":[6,227],"modeling,":[7],"with":[8,84,144,264],"linear":[9,28,51,53,81],"regression":[10,13,29,43,54,61,82,168,272],"and":[11,37,126,202,213,236,254,261],"kernel":[12,42,60,85,98],"standing":[14],"as":[15,222,251],"two":[16],"its":[18],"most":[19],"prominent":[20],"paradigms.":[21],"However,":[22],"each":[23],"approach":[24,96],"has":[25],"inherent":[26],"limitations:":[27],"highly":[31],"susceptible":[32],"to":[33,56,64,101,105,131,166,240,267],"outliers":[34],"in":[35,108,195,200,205,271],"noisy":[36,118,242],"unevenly":[38],"distributed":[39],"datasets,":[40,138],"while":[41,179,245],"often":[44],"suffers":[45],"from":[46],"overfitting.":[47],"When":[48],"data":[49,106,173,198,243],"exhibits":[50],"trends,":[52],"tends":[55],"generalize":[57],"better,":[58],"whereas":[59],"may":[62],"fail":[63],"capture":[65],"the":[66,113,128,140,150,155,181,265],"broader":[67],"patterns":[68,248],"effectively.":[69],"To":[70],"address":[71],"these":[72],"challenges,":[73],"we":[74],"propose":[75],"novel":[77],"methodology":[78],"that":[79],"integrates":[80],"principles":[83],"density":[86,99],"estimation,":[87],"termed":[88],"Kernel":[89],"Density":[90],"Estimated":[91],"Linear":[92],"(KDLR).":[94],"This":[95,120,218],"leverages":[97],"estimation":[100],"assign":[102],"higher":[103],"weights":[104],"points":[107],"dense":[109,172],"regions,":[110,174],"simultaneously":[111],"de-emphasizing":[112],"influence":[114],"sparse":[116],"or":[117],"points.":[119],"dynamic":[121],"weighting":[122],"mechanism":[123],"enhances":[124],"robustness":[125],"improves":[127],"model\u2019s":[129],"ability":[130,239],"recognize":[132],"meaningful":[133],"patterns.":[134],"Extensive":[135],"evaluations":[136],"on":[137],"including":[139,191],"California":[141],"housing":[142],"dataset":[143],"varying":[145],"levels":[146],"outliers,":[148],"demonstrate":[149],"efficacy":[151],"KDLR.":[153],"Using":[154],"mean":[156],"squared":[157],"error":[158],"metric,":[159],"KDLR":[160,187,221,250],"consistently":[161],"achieves":[162],"superior":[163],"accuracy":[164],"compared":[165],"traditional":[167],"methods.":[169],"By":[170],"prioritizing":[171],"it":[175],"captures":[176],"significant":[177],"trends":[178],"mitigating":[180],"effects":[182],"noise.":[184],"Applications":[185],"span":[188],"diverse":[189],"fields,":[190],"stock":[192],"price":[193],"prediction":[194],"finance,":[196],"patient":[197],"modeling":[199,204],"healthcare,":[201],"climate":[203],"environmental":[206],"studies":[207],"-":[208],"domains":[209],"where":[210],"robust":[211],"anomaly":[212],"noise":[214],"handling":[215],"are":[216],"critical.":[217],"research":[219,260],"establishes":[220],"transformative":[224],"tool":[225],"for":[226,257],"analytics,":[228],"offering":[229],"compelling":[231],"blend":[232],"precision,":[234],"resilience,":[235],"adaptability.":[237],"Its":[238],"manage":[241],"effectively":[244],"identifying":[246],"critical":[247],"positions":[249],"versatile":[253],"innovative":[255],"solution":[256],"both":[258],"academic":[259],"industrial":[262],"practice,":[263],"potential":[266],"redefine":[268],"best":[269],"practices":[270],"modeling.":[273]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}