{"id":"https://openalex.org/W2897180133","doi":"https://doi.org/10.1109/ijcnn.2018.8489294","title":"On Evaluating Data Preprocessing Methods for Machine Learning Models for Flight Delays","display_name":"On Evaluating Data Preprocessing Methods for Machine Learning Models for Flight Delays","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2897180133","doi":"https://doi.org/10.1109/ijcnn.2018.8489294","mag":"2897180133"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2018.8489294","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489294","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101964949","display_name":"Leonardo Marmo Moreira","orcid":"https://orcid.org/0000-0003-4967-1377"},"institutions":[{"id":"https://openalex.org/I158509141","display_name":"Federal Center for Technological Education Celso Suckow da Fonseca","ror":"https://ror.org/03j8tnm47","country_code":"BR","type":"education","lineage":["https://openalex.org/I1293487690","https://openalex.org/I158509141","https://openalex.org/I2801200668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Leonardo Moreira","raw_affiliation_strings":["CEFET/RJ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CEFET/RJ","institution_ids":["https://openalex.org/I158509141"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025386157","display_name":"Christofer Dantas","orcid":null},"institutions":[{"id":"https://openalex.org/I158509141","display_name":"Federal Center for Technological Education Celso Suckow da Fonseca","ror":"https://ror.org/03j8tnm47","country_code":"BR","type":"education","lineage":["https://openalex.org/I1293487690","https://openalex.org/I158509141","https://openalex.org/I2801200668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Christofer Dantas","raw_affiliation_strings":["CEFET/RJ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CEFET/RJ","institution_ids":["https://openalex.org/I158509141"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029028410","display_name":"Leonardo Hadlich de Oliveira","orcid":"https://orcid.org/0000-0002-1793-4075"},"institutions":[{"id":"https://openalex.org/I158509141","display_name":"Federal Center for Technological Education Celso Suckow da Fonseca","ror":"https://ror.org/03j8tnm47","country_code":"BR","type":"education","lineage":["https://openalex.org/I1293487690","https://openalex.org/I158509141","https://openalex.org/I2801200668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Leonardo Oliveira","raw_affiliation_strings":["CEFET/RJ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CEFET/RJ","institution_ids":["https://openalex.org/I158509141"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082980015","display_name":"Jorge Soares","orcid":"https://orcid.org/0000-0001-6772-9099"},"institutions":[{"id":"https://openalex.org/I158509141","display_name":"Federal Center for Technological Education Celso Suckow da Fonseca","ror":"https://ror.org/03j8tnm47","country_code":"BR","type":"education","lineage":["https://openalex.org/I1293487690","https://openalex.org/I158509141","https://openalex.org/I2801200668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Jorge Soares","raw_affiliation_strings":["CEFET/RJ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CEFET/RJ","institution_ids":["https://openalex.org/I158509141"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027399495","display_name":"Eduardo Ogasawara","orcid":"https://orcid.org/0000-0002-0466-0626"},"institutions":[{"id":"https://openalex.org/I158509141","display_name":"Federal Center for Technological Education Celso Suckow da Fonseca","ror":"https://ror.org/03j8tnm47","country_code":"BR","type":"education","lineage":["https://openalex.org/I1293487690","https://openalex.org/I158509141","https://openalex.org/I2801200668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Eduardo Ogasawara","raw_affiliation_strings":["CEFET/RJ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CEFET/RJ","institution_ids":["https://openalex.org/I158509141"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I158509141"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11489","display_name":"Air Traffic Management and Optimization","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T11489","display_name":"Air Traffic Management and Optimization","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.991100013256073,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9833999872207642,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/civil-aviation","display_name":"Civil aviation","score":0.7758525013923645},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.7711201906204224},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7262659668922424},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.6745418310165405},{"id":"https://openalex.org/keywords/aviation","display_name":"Aviation","score":0.6164457201957703},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.586437463760376},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.43864524364471436},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43285617232322693},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3786814510822296},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.3284541368484497},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32581356167793274},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.19324803352355957},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.06652191281318665}],"concepts":[{"id":"https://openalex.org/C512918668","wikidata":"https://www.wikidata.org/wiki/Q206814","display_name":"Civil aviation","level":3,"score":0.7758525013923645},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.7711201906204224},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7262659668922424},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.6745418310165405},{"id":"https://openalex.org/C74448152","wikidata":"https://www.wikidata.org/wiki/Q765633","display_name":"Aviation","level":2,"score":0.6164457201957703},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.586437463760376},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.43864524364471436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43285617232322693},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3786814510822296},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.3284541368484497},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32581356167793274},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.19324803352355957},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.06652191281318665},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2018.8489294","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489294","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322025","display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","ror":"https://ror.org/03swz6y49"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1554944419","https://openalex.org/W1569512666","https://openalex.org/W1784897978","https://openalex.org/W1968625946","https://openalex.org/W1969589598","https://openalex.org/W1972024853","https://openalex.org/W1984634390","https://openalex.org/W1990836268","https://openalex.org/W2001083091","https://openalex.org/W2033133698","https://openalex.org/W2072962100","https://openalex.org/W2078644189","https://openalex.org/W2080731889","https://openalex.org/W2089296357","https://openalex.org/W2095575776","https://openalex.org/W2096329877","https://openalex.org/W2099202896","https://openalex.org/W2104167780","https://openalex.org/W2106411961","https://openalex.org/W2109676405","https://openalex.org/W2111953021","https://openalex.org/W2113242816","https://openalex.org/W2118978333","https://openalex.org/W2119705384","https://openalex.org/W2130949063","https://openalex.org/W2138200515","https://openalex.org/W2141719669","https://openalex.org/W2148143831","https://openalex.org/W2156736878","https://openalex.org/W2250307222","https://openalex.org/W2475367951","https://openalex.org/W2487770199","https://openalex.org/W2521200999","https://openalex.org/W2538285964","https://openalex.org/W2567648555","https://openalex.org/W2570282873","https://openalex.org/W2591629812","https://openalex.org/W3099514962","https://openalex.org/W3121961986"],"related_works":["https://openalex.org/W2989490741","https://openalex.org/W3092506759","https://openalex.org/W2367545121","https://openalex.org/W4248881655","https://openalex.org/W2479339367","https://openalex.org/W2482165163","https://openalex.org/W3010890513","https://openalex.org/W120741642","https://openalex.org/W138569904","https://openalex.org/W2390914021"],"abstract_inverted_index":{"Flight":[0],"delays":[1,45,73],"cause":[2],"various":[3],"inconveniences":[4],"for":[5,111],"airlines,":[6,63],"airports,":[7,64],"and":[8,24,52,65,100],"passengers.":[9],"According":[10],"to":[11,48,76],"data":[12],"provided":[13],"by":[14,36,102],"the":[15,54,79,83,91,95,112,140,144,152],"Brazilian":[16],"National":[17],"Civil":[18],"Aviation":[19],"Agency":[20],"(ANAC),":[21],"between":[22],"2009":[23],"2015,":[25],"about":[26,157],"22%":[27],"of":[28,43,57,82,85,94,97,107,114,135,154,159],"domestic":[29],"flights":[30],"made":[31],"in":[32,71,150],"Brazil":[33],"were":[34,122],"delayed":[35],"more":[37,69],"than":[38,78],"15":[39],"minutes.":[40],"The":[41],"prediction":[42,81],"these":[44],"is":[46],"fundamental":[47],"mitigate":[49],"their":[50],"occurrence":[51,153],"optimize":[53],"decision-making":[55],"process":[56],"an":[58,104],"air":[59],"transport":[60],"system.":[61],"Particularly,":[62],"users":[66],"may":[67],"be":[68],"interested":[70],"when":[72],"are":[74],"likely":[75],"occur":[77],"accurate":[80],"absence":[84],"delays.":[86],"This":[87],"paper":[88],"focuses":[89],"on":[90],"unbalanced":[92],"distribution":[93],"classes":[96],"delay":[98,117],"(presence":[99],"absence)":[101],"performing":[103],"experimental":[105],"evaluation":[106],"several":[108],"preprocessing":[109],"methods":[110],"development":[113],"machine-learning":[115],"flight":[116,130],"classification":[118],"models.":[119],"Those":[120],"models":[121,141],"built":[123],"from":[124],"a":[125],"dataset":[126],"that":[127,142],"integrates":[128],"national":[129],"operations":[131],"with":[132],"meteorological":[133],"conditions":[134],"airports.":[136],"Our":[137],"results":[138],"indicate":[139],"applied":[143],"balancing":[145],"techniques":[146],"performed":[147],"much":[148],"better":[149],"predicting":[151],"delays,":[155],"getting":[156],"60%":[158],"hits.":[160]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
