{"id":"https://openalex.org/W4298137603","doi":"https://doi.org/10.1007/s10618-022-00863-y","title":"Decision tree boosted varying coefficient models","display_name":"Decision tree boosted varying coefficient models","publication_year":2022,"publication_date":"2022-09-30","ids":{"openalex":"https://openalex.org/W4298137603","doi":"https://doi.org/10.1007/s10618-022-00863-y"},"language":"en","primary_location":{"id":"doi:10.1007/s10618-022-00863-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-022-00863-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-022-00863-y.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"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":"Data Mining and Knowledge Discovery","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/s10618-022-00863-y.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103181294","display_name":"Yichen Zhou","orcid":"https://orcid.org/0000-0002-0596-2087"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yichen Zhou","raw_affiliation_strings":["Cornell University, Ithaca, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078275541","display_name":"Giles Hooker","orcid":"https://orcid.org/0000-0003-2648-1167"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Giles Hooker","raw_affiliation_strings":["University of California, Berkeley, USA"],"raw_orcid":"https://orcid.org/0000-0003-2648-1167","affiliations":[{"raw_affiliation_string":"University of California, Berkeley, USA","institution_ids":["https://openalex.org/I95457486"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5078275541"],"corresponding_institution_ids":["https://openalex.org/I95457486"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":2.081,"has_fulltext":true,"cited_by_count":15,"citation_normalized_percentile":{"value":0.88919869,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"36","issue":"6","first_page":"2237","last_page":"2271"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9984999895095825,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9984999895095825,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9951000213623047,"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":0.9941999912261963,"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/decision-tree","display_name":"Decision tree","score":0.5778529644012451},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.535590648651123},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5033397078514099},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48756879568099976},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4803137183189392},{"id":"https://openalex.org/keywords/parametric-model","display_name":"Parametric model","score":0.4200075566768646},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.4124450981616974},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38592982292175293},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.36028045415878296},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.29399263858795166},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2844226360321045},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16904079914093018}],"concepts":[{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5778529644012451},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.535590648651123},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5033397078514099},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48756879568099976},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4803137183189392},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.4200075566768646},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.4124450981616974},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38592982292175293},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.36028045415878296},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29399263858795166},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2844226360321045},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16904079914093018},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10618-022-00863-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-022-00863-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-022-00863-y.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"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":"Data Mining and Knowledge Discovery","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10618-022-00863-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-022-00863-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-022-00863-y.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"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":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7400000095367432}],"awards":[{"id":"https://openalex.org/G4470167390","display_name":null,"funder_award_id":"1712554","funder_id":"https://openalex.org/F4320332172","funder_display_name":"Directorate for Mathematical and Physical Sciences"}],"funders":[{"id":"https://openalex.org/F4320332172","display_name":"Directorate for Mathematical and Physical Sciences","ror":"https://ror.org/029b7h395"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4298137603.pdf","grobid_xml":"https://content.openalex.org/works/W4298137603.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W1678356000","https://openalex.org/W1788633279","https://openalex.org/W1824682467","https://openalex.org/W1835490675","https://openalex.org/W1875028359","https://openalex.org/W1969885422","https://openalex.org/W1974806455","https://openalex.org/W2006398000","https://openalex.org/W2010353172","https://openalex.org/W2045803758","https://openalex.org/W2046945713","https://openalex.org/W2070493638","https://openalex.org/W2077388943","https://openalex.org/W2086680598","https://openalex.org/W2094607565","https://openalex.org/W2130486630","https://openalex.org/W2149033360","https://openalex.org/W2151693816","https://openalex.org/W2182886880","https://openalex.org/W2208550830","https://openalex.org/W2282821441","https://openalex.org/W2509991828","https://openalex.org/W2912391306","https://openalex.org/W2949309619","https://openalex.org/W2963374347","https://openalex.org/W3093421276","https://openalex.org/W3154559866","https://openalex.org/W4289097366","https://openalex.org/W4297957988"],"related_works":["https://openalex.org/W1485630101","https://openalex.org/W2498017833","https://openalex.org/W112744582","https://openalex.org/W3033750096","https://openalex.org/W1973263317","https://openalex.org/W2031311088","https://openalex.org/W1560899470","https://openalex.org/W1580578212","https://openalex.org/W2897730733","https://openalex.org/W3210153185"],"abstract_inverted_index":{"Abstract":[0],"Varying":[1],"coefficient":[2,65,135],"models":[3,11,66,136],"are":[4,14,27,84],"a":[5,17,107],"flexible":[6,86],"extension":[7],"of":[8,16,19,23,29,38,54,77,106,110],"generic":[9],"parametric":[10,41],"whose":[12],"coefficients":[13],"functions":[15,62],"set":[18],"effect-modifying":[20],"covariates":[21],"instead":[22],"fitted":[24],"constants.":[25],"They":[26],"capable":[28],"achieving":[30],"higher":[31],"model":[32],"complexity":[33],"while":[34],"preserving":[35],"the":[36,39,52,74,94,104,130],"structure":[37],"underlying":[40],"models,":[42],"hence":[43],"generating":[44],"interpretable":[45],"predictions.":[46],"In":[47,71],"this":[48],"paper":[49],"we":[50],"study":[51],"use":[53],"gradient":[55,111],"boosted":[56,82,133],"decision":[57,123],"trees":[58,83],"as":[59,149],"those":[60],"coefficient-deciding":[61],"in":[63,93],"varying":[64,134],"with":[67],"linearly":[68],"structured":[69],"outputs.":[70],"contrast":[72],"to":[73,151],"traditional":[75],"choices":[76],"splines":[78],"or":[79],"kernel":[80],"smoothers,":[81],"more":[85],"since":[87],"they":[88],"require":[89],"no":[90],"structural":[91],"assumptions":[92,119],"effect":[95],"modifier":[96],"space.":[97],"We":[98],"introduce":[99],"our":[100],"proposed":[101,131],"method":[102],"from":[103],"perspective":[105],"localized":[108],"version":[109],"descent,":[112],"prove":[113],"its":[114],"theoretical":[115],"consistency":[116],"under":[117],"mild":[118],"commonly":[120],"adapted":[121],"by":[122,141],"tree":[124,132],"research,":[125],"and":[126,147],"empirically":[127],"demonstrate":[128],"that":[129],"achieve":[137],"high":[138],"performance":[139],"qualified":[140],"their":[142],"training":[143],"speed,":[144],"prediction":[145],"accuracy":[146],"intelligibility":[148],"compared":[150],"several":[152],"benchmark":[153],"algorithms.":[154]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}