{"id":"https://openalex.org/W7148006879","doi":"https://doi.org/10.1109/cnml68938.2026.11453306","title":"KRRCR: Kernel Ridge Regression for Interval-Valued Data with Application to Air Quality Forecasting","display_name":"KRRCR: Kernel Ridge Regression for Interval-Valued Data with Application to Air Quality Forecasting","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7148006879","doi":"https://doi.org/10.1109/cnml68938.2026.11453306"},"language":null,"primary_location":{"id":"doi:10.1109/cnml68938.2026.11453306","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11453306","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","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/A5100694840","display_name":"Min Yang","orcid":"https://orcid.org/0000-0002-9034-3175"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mengmeng Yang","raw_affiliation_strings":["Beijing Institute of Technology,School of Mathematics and Statistics,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,School of Mathematics and Statistics,Beijing,China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5132711572","display_name":"Shuangwen Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shuangwen Chen","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100694840"],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.94419714,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1008","last_page":"1011"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9563000202178955,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9563000202178955,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.0071000000461936,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10190","display_name":"Air Quality and Health Impacts","score":0.004600000102072954,"subfield":{"id":"https://openalex.org/subfields/2307","display_name":"Health, Toxicology and Mutagenesis"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7645999789237976},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.498199999332428},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.46140000224113464},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4036000072956085},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.3873000144958496},{"id":"https://openalex.org/keywords/index","display_name":"Index (typography)","score":0.3546999990940094},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.3472999930381775},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.334199994802475},{"id":"https://openalex.org/keywords/ridge","display_name":"Ridge","score":0.33169999718666077}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7645999789237976},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5404999852180481},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.498199999332428},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4683000147342682},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.46140000224113464},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4036000072956085},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3873000144958496},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.3546999990940094},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3472999930381775},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.334199994802475},{"id":"https://openalex.org/C32277403","wikidata":"https://www.wikidata.org/wiki/Q740445","display_name":"Ridge","level":2,"score":0.33169999718666077},{"id":"https://openalex.org/C126314574","wikidata":"https://www.wikidata.org/wiki/Q2364111","display_name":"Air quality index","level":2,"score":0.3224000036716461},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32190001010894775},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30630001425743103},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C200695384","wikidata":"https://www.wikidata.org/wiki/Q1739319","display_name":"Kernel regression","level":3,"score":0.30059999227523804},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.29760000109672546},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2939999997615814},{"id":"https://openalex.org/C197640229","wikidata":"https://www.wikidata.org/wiki/Q2534066","display_name":"Predictability","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.2842000126838684},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2784999907016754},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.27630001306533813},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27549999952316284},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.2750000059604645},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2687999904155731},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.2567000091075897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cnml68938.2026.11453306","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11453306","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1966139378","https://openalex.org/W1972745723","https://openalex.org/W1975510656","https://openalex.org/W2039480584","https://openalex.org/W2072242542","https://openalex.org/W2093709255","https://openalex.org/W2119981768","https://openalex.org/W2147184730","https://openalex.org/W2292731875","https://openalex.org/W3195681742","https://openalex.org/W3197605526","https://openalex.org/W4390727700","https://openalex.org/W4406711279","https://openalex.org/W4408831483"],"related_works":[],"abstract_inverted_index":{"Interval-valued":[0],"data":[1,14,131],"are":[2,64],"ubiquitous":[3],"in":[4,21],"fields":[5],"such":[6,13],"as":[7],"finance":[8],"and":[9,68,85,123,132],"environmental":[10],"monitoring.":[11],"Forecasting":[12],"is":[15],"essential":[16],"for":[17,32,83,93],"uncertainty":[18],"quantification,":[19],"particularly":[20],"Air":[22],"Quality":[23],"Index":[24,109],"(AQI)":[25],"prediction,":[26],"where":[27],"it":[28],"provides":[29],"critical":[30],"support":[31],"risk":[33],"management.":[34],"Existing":[35],"methods":[36],"within":[37],"the":[38,52,106,117],"prevalent":[39],"center-range":[40],"framework":[41],"face":[42],"two":[43],"primary":[44],"challenges:":[45],"linear":[46],"models":[47],"(e.g.,":[48,62],"CRM,":[49],"Lasso-IR)":[50],"lack":[51],"capacity":[53],"to":[54,66,99],"capture":[55],"complex":[56],"nonlinearities,":[57],"while":[58],"nonlinear":[59,94],"kernel":[60,91],"approaches":[61],"IKRCR)":[63],"prone":[65],"overfitting":[67],"offer":[69],"no":[70],"optimality":[71],"guarantees.":[72],"To":[73],"address":[74],"these":[75],"limitations,":[76],"this":[77],"paper":[78],"proposes":[79],"Kernel":[80],"Ridge":[81],"Regression":[82],"Center":[84],"Range":[86],"information":[87],"(KRRCR).":[88],"KR-RCR":[89],"integrates":[90],"tricks":[92],"mapping":[95],"with":[96],"ridge":[97],"regularization":[98],"ensure":[100],"robust":[101],"generalization.":[102],"Furthermore,":[103],"we":[104],"propose":[105],"Symmetric":[107],"Overlap":[108],"(SOI),":[110],"a":[111,133],"novel":[112],"evaluation":[113],"metric":[114],"that":[115,139],"harmonizes":[116],"trade-off":[118],"between":[119],"coverage":[120],"rate":[121,125],"(CR)":[122],"efficiency":[124],"(ER).":[126],"Comprehensive":[127],"experiments":[128],"on":[129],"synthetic":[130],"real-world":[134],"Beijing":[135],"AQI":[136],"dataset":[137],"demonstrate":[138],"KRRCR":[140],"outperforms":[141],"established":[142],"benchmarks.":[143]},"counts_by_year":[],"updated_date":"2026-04-02T13:53:19.096889","created_date":"2026-04-02T00:00:00"}