{"id":"https://openalex.org/W3110285720","doi":"https://doi.org/10.1109/access.2020.3041367","title":"A Comparison Framework of Machine Learning Algorithms for Mixed-Type Variables Datasets: A Case Study on Tire-Performances Prediction","display_name":"A Comparison Framework of Machine Learning Algorithms for Mixed-Type Variables Datasets: A Case Study on Tire-Performances Prediction","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3110285720","doi":"https://doi.org/10.1109/access.2020.3041367","mag":"3110285720"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3041367","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3041367","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09273039.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09273039.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082308139","display_name":"Leonardo Guti\u00e9rrez-G\u00f3mez","orcid":"https://orcid.org/0000-0001-6405-3775"},"institutions":[{"id":"https://openalex.org/I4210112527","display_name":"Luxembourg Institute of Science and Technology","ror":"https://ror.org/01t178j62","country_code":"LU","type":"nonprofit","lineage":["https://openalex.org/I4210112527"]}],"countries":["LU"],"is_corresponding":true,"raw_author_name":"Leonardo Gutierrez-Gomez","raw_affiliation_strings":["Luxembourg Institute of Science and Technology (LIST), Esch-sur-Alzette, Luxembourg"],"affiliations":[{"raw_affiliation_string":"Luxembourg Institute of Science and Technology (LIST), Esch-sur-Alzette, Luxembourg","institution_ids":["https://openalex.org/I4210112527"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058675624","display_name":"Frank Petry","orcid":"https://orcid.org/0000-0002-7951-8712"},"institutions":[{"id":"https://openalex.org/I4210131493","display_name":"Goodyear (Luxembourg)","ror":"https://ror.org/03sdd1k49","country_code":"LU","type":"company","lineage":["https://openalex.org/I4210131493","https://openalex.org/I4210143271"]}],"countries":["LU"],"is_corresponding":false,"raw_author_name":"Frank Petry","raw_affiliation_strings":["Goodyear Innovation Center* Luxembourg, Colmar-Berg, Luxembourg"],"affiliations":[{"raw_affiliation_string":"Goodyear Innovation Center* Luxembourg, Colmar-Berg, Luxembourg","institution_ids":["https://openalex.org/I4210131493"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041439610","display_name":"Djamel Khadraoui","orcid":"https://orcid.org/0000-0003-1054-1612"},"institutions":[{"id":"https://openalex.org/I4210112527","display_name":"Luxembourg Institute of Science and Technology","ror":"https://ror.org/01t178j62","country_code":"LU","type":"nonprofit","lineage":["https://openalex.org/I4210112527"]}],"countries":["LU"],"is_corresponding":false,"raw_author_name":"Djamel Khadraoui","raw_affiliation_strings":["Luxembourg Institute of Science and Technology (LIST), Esch-sur-Alzette, Luxembourg"],"affiliations":[{"raw_affiliation_string":"Luxembourg Institute of Science and Technology (LIST), Esch-sur-Alzette, Luxembourg","institution_ids":["https://openalex.org/I4210112527"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5082308139"],"corresponding_institution_ids":["https://openalex.org/I4210112527"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.2346,"has_fulltext":true,"cited_by_count":22,"citation_normalized_percentile":{"value":0.84579412,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"8","issue":null,"first_page":"214902","last_page":"214914"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.988099992275238,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.988099992275238,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9666000008583069,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9541000127792358,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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":0.7337055206298828},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.7260446548461914},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6250593662261963},{"id":"https://openalex.org/keywords/automotive-industry","display_name":"Automotive industry","score":0.5054738521575928},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5031589865684509},{"id":"https://openalex.org/keywords/multi-label-classification","display_name":"Multi-label classification","score":0.4889563322067261},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4865111708641052},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47592002153396606},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4737185835838318},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.4581941068172455},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.44225233793258667},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4372766613960266},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.146937757730484},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11573830246925354},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09443968534469604}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7337055206298828},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.7260446548461914},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6250593662261963},{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.5054738521575928},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5031589865684509},{"id":"https://openalex.org/C2776482837","wikidata":"https://www.wikidata.org/wiki/Q3553958","display_name":"Multi-label classification","level":2,"score":0.4889563322067261},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4865111708641052},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47592002153396606},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4737185835838318},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4581941068172455},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.44225233793258667},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4372766613960266},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.146937757730484},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11573830246925354},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09443968534469604},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.3041367","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3041367","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09273039.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c2cee0eecd7a4427a41e27bf369d70d2","is_oa":true,"landing_page_url":"https://doaj.org/article/c2cee0eecd7a4427a41e27bf369d70d2","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 214902-214914 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3041367","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3041367","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09273039.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3110285720.pdf","grobid_xml":"https://content.openalex.org/works/W3110285720.grobid-xml"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W175671933","https://openalex.org/W1510073064","https://openalex.org/W1565746575","https://openalex.org/W1678356000","https://openalex.org/W1985258161","https://openalex.org/W1994197834","https://openalex.org/W2025157389","https://openalex.org/W2027758424","https://openalex.org/W2046522518","https://openalex.org/W2052611008","https://openalex.org/W2070996757","https://openalex.org/W2079324170","https://openalex.org/W2093604883","https://openalex.org/W2097998348","https://openalex.org/W2100233488","https://openalex.org/W2134452881","https://openalex.org/W2137690751","https://openalex.org/W2152978141","https://openalex.org/W2156909104","https://openalex.org/W2169996835","https://openalex.org/W2410531948","https://openalex.org/W2464234006","https://openalex.org/W2744527439","https://openalex.org/W2759373267","https://openalex.org/W2765449478","https://openalex.org/W2768035349","https://openalex.org/W2799598492","https://openalex.org/W2809080621","https://openalex.org/W2890303623","https://openalex.org/W2900404321","https://openalex.org/W2911964244","https://openalex.org/W2999595216","https://openalex.org/W3008277736","https://openalex.org/W3008473879","https://openalex.org/W3021030633","https://openalex.org/W3022413497","https://openalex.org/W3092363736","https://openalex.org/W3099131594","https://openalex.org/W3114671431","https://openalex.org/W4213113494","https://openalex.org/W4242841269","https://openalex.org/W6674385629","https://openalex.org/W6744729954"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W2389073067","https://openalex.org/W3114793362","https://openalex.org/W4224009465","https://openalex.org/W4286629047","https://openalex.org/W4306321456","https://openalex.org/W4285260836","https://openalex.org/W2118072550","https://openalex.org/W3214804357"],"abstract_inverted_index":{"Many":[0],"engineering":[1],"applications":[2],"in":[3,79,128],"the":[4,85,129,147],"automotive,":[5],"aeronautic,":[6],"rubber,":[7],"mechanics,":[8],"and":[9,21,38,44,50,139,154,185,196],"manufacturing":[10],"industries":[11],"collect":[12],"multiple":[13,99,134,178],"datasets":[14,104],"measuring":[15],"physical":[16],"relations":[17],"between":[18,152,189],"input":[19],"variables":[20,97,127,169],"performances":[22,173],"for":[23,64,92,165,192],"modeling":[24],"purposes.":[25],"The":[26],"challenge":[27],"relies":[28],"on":[29,174],"that":[30],"such":[31],"data":[32,66],"is":[33,67,74],"often":[34],"highly":[35],"dimensional,":[36],"non-linear":[37],"contain":[39],"mixed":[40,126],"variables,":[41],"i.e.,":[42],"numerical":[43],"categorical":[45],"features,":[46],"requiring":[47],"specific":[48],"algorithms":[49,91,120],"encoding":[51,122,155],"schemes":[52,123],"to":[53,76,109,124,145],"perform":[54,110],"regression":[55,80,93,175],"task":[56],"efficiently.":[57],"Moreover,":[58],"defining":[59],"an":[60],"appropriated":[61],"similarity":[62,187],"criterion":[63],"mixed-type":[65,96,168],"a":[68,106,111,161,183],"non-trivial":[69],"task,":[70],"especially":[71],"when":[72],"it":[73],"meant":[75],"be":[77],"used":[78,144],"problems.":[81],"This":[82],"paper":[83],"discusses":[84],"use":[86,102],"of":[87,116,131,149,163],"different":[88,117],"machine":[89,118],"learning":[90,119],"problems,":[94],"involving":[95],"across":[98,133],"datasets.":[100,136,179],"We":[101],"tire-related":[103,135],"as":[105,160],"case":[107],"study":[108],"rigorous,":[112],"statistically":[113],"founded":[114],"comparison":[115],"with":[121],"handle":[125],"prediction":[130],"tire-performances":[132],"Friedman's":[137],"statistic":[138],"Nemenyi":[140],"post-hoc":[141],"tests":[142],"are":[143],"test":[146],"significance":[148],"performance":[150],"differences":[151],"techniques":[153],"strategies.":[156],"Our":[157],"contributions":[158],"come":[159],"series":[162],"recommendations":[164],"handling":[166],"efficiently":[167],"while":[170],"achieving":[171],"high":[172],"tasks":[176],"over":[177],"Furthermore,":[180],"we":[181],"provide":[182],"flexible":[184],"efficient":[186],"function":[188],"tires":[190],"useful":[191],"tire":[193],"comparison,":[194],"prediction,":[195],"retrieval":[197],"tasks.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-28T08:17:26.163206","created_date":"2025-10-10T00:00:00"}
