{"id":"https://openalex.org/W3200808408","doi":"https://doi.org/10.1007/s11634-021-00463-6","title":"A comparison of two dissimilarity functions for mixed-type predictor variables in the $$\\delta $$-machine","display_name":"A comparison of two dissimilarity functions for mixed-type predictor variables in the $$\\delta $$-machine","publication_year":2021,"publication_date":"2021-09-16","ids":{"openalex":"https://openalex.org/W3200808408","doi":"https://doi.org/10.1007/s11634-021-00463-6","mag":"3200808408"},"language":"en","primary_location":{"id":"doi:10.1007/s11634-021-00463-6","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11634-021-00463-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11634-021-00463-6.pdf","source":{"id":"https://openalex.org/S4210175730","display_name":"Advances in Data Analysis and Classification","issn_l":"1862-5355","issn":["1862-5355","1862-5347"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Data Analysis and Classification","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11634-021-00463-6.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083663215","display_name":"Beibei Yuan","orcid":"https://orcid.org/0000-0001-8618-9366"},"institutions":[{"id":"https://openalex.org/I121797337","display_name":"Leiden University","ror":"https://ror.org/027bh9e22","country_code":"NL","type":"education","lineage":["https://openalex.org/I121797337"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Beibei Yuan","raw_affiliation_strings":["Institute of Psychology, Leiden University, Wassenaarseweg 52, 2333 AK, Leiden, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0001-8618-9366","affiliations":[{"raw_affiliation_string":"Institute of Psychology, Leiden University, Wassenaarseweg 52, 2333 AK, Leiden, The Netherlands","institution_ids":["https://openalex.org/I121797337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081431698","display_name":"Willem J. Heiser","orcid":null},"institutions":[{"id":"https://openalex.org/I121797337","display_name":"Leiden University","ror":"https://ror.org/027bh9e22","country_code":"NL","type":"education","lineage":["https://openalex.org/I121797337"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Willem Heiser","raw_affiliation_strings":["Institute of Psychology, Leiden University, Wassenaarseweg 52, 2333 AK, Leiden, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Psychology, Leiden University, Wassenaarseweg 52, 2333 AK, Leiden, The Netherlands","institution_ids":["https://openalex.org/I121797337"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081476149","display_name":"Mark de Rooij","orcid":"https://orcid.org/0000-0001-7308-6210"},"institutions":[{"id":"https://openalex.org/I121797337","display_name":"Leiden University","ror":"https://ror.org/027bh9e22","country_code":"NL","type":"education","lineage":["https://openalex.org/I121797337"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Mark de Rooij","raw_affiliation_strings":["Institute of Psychology, Leiden University, Wassenaarseweg 52, 2333 AK, Leiden, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Psychology, Leiden University, Wassenaarseweg 52, 2333 AK, Leiden, The Netherlands","institution_ids":["https://openalex.org/I121797337"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5083663215"],"corresponding_institution_ids":["https://openalex.org/I121797337"],"apc_list":{"value":2990,"currency":"USD","value_usd":2990},"apc_paid":{"value":2990,"currency":"USD","value_usd":2990},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13524321,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"16","issue":"4","first_page":"875","last_page":"907"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9950000047683716,"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"}},"topics":[{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9950000047683716,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9944999814033508,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9941999912261963,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5725594162940979},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5049641728401184},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4838913381099701},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.48379892110824585},{"id":"https://openalex.org/keywords/type","display_name":"Type (biology)","score":0.4744837284088135},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45777082443237305},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.42471233010292053},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3783243000507355},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.37299397587776184}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5725594162940979},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5049641728401184},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4838913381099701},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.48379892110824585},{"id":"https://openalex.org/C2777299769","wikidata":"https://www.wikidata.org/wiki/Q3707858","display_name":"Type (biology)","level":2,"score":0.4744837284088135},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45777082443237305},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.42471233010292053},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3783243000507355},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.37299397587776184},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1007/s11634-021-00463-6","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11634-021-00463-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11634-021-00463-6.pdf","source":{"id":"https://openalex.org/S4210175730","display_name":"Advances in Data Analysis and Classification","issn_l":"1862-5355","issn":["1862-5355","1862-5347"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Data Analysis and Classification","raw_type":"journal-article"},{"id":"pmh:oai:scholarlypublications.universiteitleiden.nl:item_3216829","is_oa":true,"landing_page_url":"https://hdl.handle.net/1887/3216829","pdf_url":"https://hdl.handle.net/1887/3216829","source":{"id":"https://openalex.org/S4306400850","display_name":"Leiden Repository (Leiden University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I121797337","host_organization_name":"Leiden University","host_organization_lineage":["https://openalex.org/I121797337"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Advances in Data Analysis and Classification","raw_type":"Article / Letter to editor"},{"id":"pmh:oai:RePEc:spr:advdac:v:16:y:2022:i:4:d:10.1007_s11634-021-00463-6","is_oa":false,"landing_page_url":"http://link.springer.com/10.1007/s11634-021-00463-6","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},{"id":"pmh:ul:oai:scholarlypublications.universiteitleiden.nl:item_3216829","is_oa":true,"landing_page_url":"http://hdl.handle.net/1887/3216829","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Advances in Data Analysis and Classification","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1007/s11634-021-00463-6","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11634-021-00463-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11634-021-00463-6.pdf","source":{"id":"https://openalex.org/S4210175730","display_name":"Advances in Data Analysis and Classification","issn_l":"1862-5355","issn":["1862-5355","1862-5347"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Data Analysis and Classification","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1343927835","display_name":null,"funder_award_id":"022.005.022","funder_id":"https://openalex.org/F4320321800","funder_display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek"}],"funders":[{"id":"https://openalex.org/F4320321800","display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","ror":"https://ror.org/04jsz6e67"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3200808408.pdf","grobid_xml":"https://content.openalex.org/works/W3200808408.grobid-xml"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W1480708938","https://openalex.org/W1493454437","https://openalex.org/W1545647341","https://openalex.org/W1590246454","https://openalex.org/W1594031697","https://openalex.org/W1678356000","https://openalex.org/W1870624945","https://openalex.org/W1974406774","https://openalex.org/W1982286843","https://openalex.org/W1989164753","https://openalex.org/W2032610031","https://openalex.org/W2047777605","https://openalex.org/W2049962488","https://openalex.org/W2056884786","https://openalex.org/W2067752346","https://openalex.org/W2086618114","https://openalex.org/W2097360283","https://openalex.org/W2100805904","https://openalex.org/W2107031757","https://openalex.org/W2108949035","https://openalex.org/W2109943925","https://openalex.org/W2112076978","https://openalex.org/W2117812871","https://openalex.org/W2119821739","https://openalex.org/W2123402141","https://openalex.org/W2127218421","https://openalex.org/W2135046866","https://openalex.org/W2149230623","https://openalex.org/W2151572298","https://openalex.org/W2157656721","https://openalex.org/W2158698691","https://openalex.org/W2201241732","https://openalex.org/W2299467264","https://openalex.org/W2319824271","https://openalex.org/W2479500547","https://openalex.org/W2487770199","https://openalex.org/W2767914965","https://openalex.org/W2911964244","https://openalex.org/W2912934387","https://openalex.org/W2969395809","https://openalex.org/W2979906557","https://openalex.org/W2999729612","https://openalex.org/W3089164792","https://openalex.org/W4212883601","https://openalex.org/W4239510810","https://openalex.org/W4285719420","https://openalex.org/W4294541781","https://openalex.org/W4298304654","https://openalex.org/W4300870773"],"related_works":["https://openalex.org/W4233153962","https://openalex.org/W3094090087","https://openalex.org/W2048637055","https://openalex.org/W1983020508","https://openalex.org/W4225307033","https://openalex.org/W4376626979","https://openalex.org/W2195347453","https://openalex.org/W4220812971","https://openalex.org/W1491722193","https://openalex.org/W3161269547"],"abstract_inverted_index":{"Abstract":[0],"The":[1,170,192,273],"$$\\delta":[2,45,63,134,174,212,249,278],"$$":[3,46,64,135,175,213,250,279],"<mml:math":[4,47,65,136,176,214,251,280],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"><mml:mi>\u03b4</mml:mi></mml:math>":[5,48,66,137,177,215,252,281],"-machine":[6,49,67,138,178,216,253,282],"is":[7],"a":[8,27,79,97,103,106,284],"statistical":[9],"learning":[10],"tool":[11],"for":[12,83,110,222],"classification":[13,266,271],"based":[14],"on":[15],"dissimilarities":[16],"or":[17],"distances":[18],"between":[19,287],"profiles":[20,25],"of":[21,26,96,121,132,154,159,165,172,187],"the":[22,44,62,119,122,129,133,152,157,163,173,180,197,205,211,224,235,243,248,265,277],"observations":[23],"to":[24,52,68,139,147,254],"representation":[28,188,225],"set,":[29,226],"which":[30,87],"was":[31,50,88,168,190],"proposed":[32],"by":[33,90],"Yuan":[34],"et":[35],"al.":[36],"(J":[37],"Claasif":[38],"36(3):":[39],"442\u2013470,":[40],"2019).":[41],"So":[42],"far,":[43],"restricted":[51],"continuous":[53],"predictor":[54,75,155,166],"variables":[55,86,167],"only.":[56],"In":[57,114],"this":[58],"article,":[59],"we":[60,117,127],"extend":[61],"handle":[69],"continuous,":[70],"ordinal,":[71],"nominal,":[72],"and":[73,126,162,184,220,263,289],"binary":[74],"variables.":[76,113],"We":[77,100,143,245],"utilized":[78],"tailored":[80],"dissimilarity":[81,108,124,182,200,208],"function":[82,109,201],"mixed":[84,111],"type":[85,112,153],"defined":[89],"Gower.":[91],"This":[92],"measure":[93],"has":[94,283],"properties":[95],"Manhattan":[98],"distance.":[99],"develop,":[101],"in":[102,261],"similar":[104],"vein,":[105],"Euclidean":[107,199],"simulation":[115,193],"studies":[116,194],"compare":[118,128],"performance":[120,131,171,267],"two":[123,148,181],"functions":[125,183],"predictive":[130],"logistic":[140,218],"regression":[141],"models.":[142],"generated":[144],"data":[145],"according":[146],"population":[149],"distributions":[150],"where":[151],"variables,":[156,161],"distribution":[158],"categorical":[160],"number":[164],"varied.":[169],"using":[179,237],"different":[185],"types":[186],"set":[189],"investigated.":[191],"showed":[195,275],"that":[196,210,221,276],"adjusted":[198,206],"performed":[202],"better":[203],"than":[204,234],"Gower":[207],"function;":[209],"outperformed":[217],"regression;":[219],"constructing":[223],"K":[227,238],"-medoids":[228],"clustering":[229,240],"achieved":[230],"fewer":[231],"active":[232],"exemplars":[233],"one":[236],"-means":[239],"while":[241],"maintaining":[242],"accuracy.":[244],"also":[246],"applied":[247],"an":[255],"empirical":[256],"example,":[257],"discussed":[258],"its":[259],"interpretation":[260],"detail,":[262],"compared":[264],"with":[268],"five":[269],"other":[270],"methods.":[272],"results":[274],"good":[285],"balance":[286],"accuracy":[288],"interpretability.":[290]},"counts_by_year":[],"updated_date":"2026-08-04T08:18:43.703281","created_date":"2021-09-27T00:00:00"}
