{"id":"https://openalex.org/W2522669593","doi":"https://doi.org/10.1109/ijcnn.2016.7727541","title":"Clustering-based feature selection for black-box weather temperature prediction","display_name":"Clustering-based feature selection for black-box weather temperature prediction","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2522669593","doi":"https://doi.org/10.1109/ijcnn.2016.7727541","mag":"2522669593"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2016.7727541","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727541","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://lirias.kuleuven.be/handle/123456789/542288","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024115772","display_name":"Zahra Karevan","orcid":null},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":true,"raw_author_name":"Zahra Karevan","raw_affiliation_strings":["KU Leuven, ESAT-STADIUS, Leuven, Belgium"],"affiliations":[{"raw_affiliation_string":"KU Leuven, ESAT-STADIUS, Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078854904","display_name":"Johan A. K. Suykens","orcid":"https://orcid.org/0000-0002-8846-6352"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Johan A.K. Suykens","raw_affiliation_strings":["KU Leuven, ESAT-STADIUS, Leuven, Belgium"],"affiliations":[{"raw_affiliation_string":"KU Leuven, ESAT-STADIUS, Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5024115772"],"corresponding_institution_ids":["https://openalex.org/I99464096"],"apc_list":null,"apc_paid":null,"fwci":0.9689,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.81007062,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"224","issue":null,"first_page":"2722","last_page":"2729"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9868000149726868,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9799000024795532,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7617564797401428},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6918097734451294},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6462258696556091},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6343902349472046},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6174678206443787},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5554323792457581},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5161893963813782},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.4863465428352356},{"id":"https://openalex.org/keywords/elastic-net-regularization","display_name":"Elastic net regularization","score":0.4637952446937561},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4560723900794983},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43820685148239136},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.4221949577331543},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4144916832447052},{"id":"https://openalex.org/keywords/weather-forecasting","display_name":"Weather forecasting","score":0.4103166460990906},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18190565705299377},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.1528768241405487},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08563262224197388}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7617564797401428},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6918097734451294},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6462258696556091},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6343902349472046},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6174678206443787},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5554323792457581},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5161893963813782},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4863465428352356},{"id":"https://openalex.org/C203868755","wikidata":"https://www.wikidata.org/wiki/Q5353562","display_name":"Elastic net regularization","level":3,"score":0.4637952446937561},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4560723900794983},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43820685148239136},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.4221949577331543},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4144916832447052},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.4103166460990906},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18190565705299377},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.1528768241405487},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08563262224197388},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn.2016.7727541","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727541","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:lirias2repo.kuleuven.be:123456789/542288","is_oa":true,"landing_page_url":"https://lirias.kuleuven.be/handle/123456789/542288","pdf_url":null,"source":{"id":"https://openalex.org/S4306401954","display_name":"Lirias (KU Leuven)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I99464096","host_organization_name":"KU Leuven","host_organization_lineage":["https://openalex.org/I99464096"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IJCNN 2016, Vancouver, Canada, July 2016","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"pmh:oai:lirias2repo.kuleuven.be:123456789/542288","is_oa":true,"landing_page_url":"https://lirias.kuleuven.be/handle/123456789/542288","pdf_url":null,"source":{"id":"https://openalex.org/S4306401954","display_name":"Lirias (KU Leuven)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I99464096","host_organization_name":"KU Leuven","host_organization_lineage":["https://openalex.org/I99464096"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IJCNN 2016, Vancouver, Canada, July 2016","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[{"display_name":"Climate action","score":0.6399999856948853,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W77620717","https://openalex.org/W1496317909","https://openalex.org/W1596717185","https://openalex.org/W1635142054","https://openalex.org/W1789155650","https://openalex.org/W1833117266","https://openalex.org/W2014158063","https://openalex.org/W2042336720","https://openalex.org/W2047028564","https://openalex.org/W2056763477","https://openalex.org/W2058898560","https://openalex.org/W2063202388","https://openalex.org/W2105681109","https://openalex.org/W2122825543","https://openalex.org/W2135046866","https://openalex.org/W2168175751","https://openalex.org/W3031759008","https://openalex.org/W4255455317","https://openalex.org/W4301501800","https://openalex.org/W6603134976","https://openalex.org/W6778580933"],"related_works":["https://openalex.org/W4213314600","https://openalex.org/W3211319599","https://openalex.org/W2122825543","https://openalex.org/W2997212488","https://openalex.org/W2380784125","https://openalex.org/W4387870091","https://openalex.org/W2765894738","https://openalex.org/W1997711767","https://openalex.org/W2991486385","https://openalex.org/W3006570723"],"abstract_inverted_index":{"Reliable":[0],"weather":[1,138],"forecasting":[2],"is":[3,26,40,62,85,127,133,153],"one":[4],"of":[5,15,103,116,148],"the":[6,47,57,89,92,101,104,111,114,117,130,136,143,146,149],"challenging":[7],"tasks":[8],"that":[9,129],"deals":[10],"with":[11,135],"a":[12,22,65],"large":[13],"number":[14],"observations":[16],"and":[17,76,120,125],"features.":[18],"In":[19,110],"this":[20],"paper,":[21],"data-driven":[23,131],"modeling":[24],"technique":[25,132],"proposed":[27,118],"for":[28,53],"temperature":[29,139,150],"prediction.":[30],"To":[31],"investigate":[32],"local":[33],"learning,":[34],"Soft":[35],"Kernel":[36],"Spectral":[37],"Clustering":[38],"(SKSC)":[39],"used":[41,52,86],"to":[42,46,50,56,87,107],"find":[43],"similar":[44],"samples":[45],"test":[48,105],"point":[49,106],"be":[51],"training.":[54],"Due":[55],"high":[58],"dimensionality,":[59],"Elastic":[60],"net":[61],"employed":[63],"as":[64],"feature":[66],"selection":[67],"approach.":[68],"Features":[69],"are":[70,97,123],"selected":[71],"in":[72,151],"each":[73,108],"cluster":[74],"independently":[75],"then,":[77],"Least":[78],"Squares":[79],"Support":[80],"Vector":[81],"Machines":[82],"(LS-SVM)":[83],"regression":[84],"learn":[88],"data.":[90],"Finally,":[91],"predicted":[93],"values":[94],"by":[95],"LS-SVMs":[96],"averaged":[98],"based":[99],"on":[100],"membership":[102],"cluster.":[109],"experimental":[112],"results,":[113],"performance":[115],"method":[119],"\u201cWeather":[121],"underground\u201d":[122],"compared":[124],"it":[126],"shown":[128],"competitive":[134],"existing":[137],"prediction":[140,147],"sites.":[141],"For":[142],"case":[144],"study,":[145],"Brussels":[152],"considered.":[154]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}