{"id":"https://openalex.org/W3004959911","doi":"https://doi.org/10.1108/ijcs-10-2019-0029","title":"RegBoost: a gradient boosted multivariate regression algorithm","display_name":"RegBoost: a gradient boosted multivariate regression algorithm","publication_year":2020,"publication_date":"2020-01-31","ids":{"openalex":"https://openalex.org/W3004959911","doi":"https://doi.org/10.1108/ijcs-10-2019-0029","mag":"3004959911"},"language":"en","primary_location":{"id":"doi:10.1108/ijcs-10-2019-0029","is_oa":true,"landing_page_url":"https://doi.org/10.1108/ijcs-10-2019-0029","pdf_url":null,"source":{"id":"https://openalex.org/S4210199319","display_name":"International Journal of Crowd Science","issn_l":"2398-7294","issn":["2398-7294"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Crowd Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1108/ijcs-10-2019-0029","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100698115","display_name":"Li Wen","orcid":"https://orcid.org/0000-0003-3783-3826"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wen Li","raw_affiliation_strings":["College of Electronics and Information Engineering, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"College of Electronics and Information Engineering, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100391989","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0002-5038-8563"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["College of Electronics and Information Engineering, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"College of Electronics and Information Engineering, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112504577","display_name":"Wenjun Huo","orcid":null},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenjun Huo","raw_affiliation_strings":["College of Electronics and Information Engineering, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"College of Electronics and Information Engineering, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100698115"],"corresponding_institution_ids":["https://openalex.org/I116953780"],"apc_list":null,"apc_paid":null,"fwci":1.1014,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.78192208,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"4","issue":"1","first_page":"60","last_page":"72"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9976000189781189,"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":0.9976000189781189,"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/T10057","display_name":"Face and Expression Recognition","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9865999817848206,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/bayesian-multivariate-linear-regression","display_name":"Bayesian multivariate linear regression","score":0.774569034576416},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.7712931632995605},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.7028193473815918},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6612626910209656},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5915467143058777},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5780097246170044},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.5681319832801819},{"id":"https://openalex.org/keywords/multivariate-adaptive-regression-splines","display_name":"Multivariate adaptive regression splines","score":0.5332376956939697},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5165438055992126},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.4718106985092163},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4482663571834564},{"id":"https://openalex.org/keywords/proper-linear-model","display_name":"Proper linear model","score":0.41059795022010803},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.40433815121650696},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4026019871234894},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39082521200180054},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3357025384902954},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.314556360244751},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.3095053434371948}],"concepts":[{"id":"https://openalex.org/C64946054","wikidata":"https://www.wikidata.org/wiki/Q4874476","display_name":"Bayesian multivariate linear regression","level":3,"score":0.774569034576416},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.7712931632995605},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.7028193473815918},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6612626910209656},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5915467143058777},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5780097246170044},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.5681319832801819},{"id":"https://openalex.org/C44882253","wikidata":"https://www.wikidata.org/wiki/Q3455882","display_name":"Multivariate adaptive regression splines","level":4,"score":0.5332376956939697},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5165438055992126},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4718106985092163},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4482663571834564},{"id":"https://openalex.org/C32224588","wikidata":"https://www.wikidata.org/wiki/Q7250175","display_name":"Proper linear model","level":4,"score":0.41059795022010803},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.40433815121650696},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4026019871234894},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39082521200180054},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3357025384902954},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.314556360244751},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.3095053434371948}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1108/ijcs-10-2019-0029","is_oa":true,"landing_page_url":"https://doi.org/10.1108/ijcs-10-2019-0029","pdf_url":null,"source":{"id":"https://openalex.org/S4210199319","display_name":"International Journal of Crowd Science","issn_l":"2398-7294","issn":["2398-7294"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Crowd Science","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:404e16045e7f42c095162ef43e544445","is_oa":true,"landing_page_url":"https://doaj.org/article/404e16045e7f42c095162ef43e544445","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":"International Journal of Crowd Science, Vol 4, Iss 1, Pp 60-72 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1108/ijcs-10-2019-0029","is_oa":true,"landing_page_url":"https://doi.org/10.1108/ijcs-10-2019-0029","pdf_url":null,"source":{"id":"https://openalex.org/S4210199319","display_name":"International Journal of Crowd Science","issn_l":"2398-7294","issn":["2398-7294"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Crowd Science","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6100000143051147,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1602556108","https://openalex.org/W1678356000","https://openalex.org/W2033529337","https://openalex.org/W2039240409","https://openalex.org/W2050049213","https://openalex.org/W2064769840","https://openalex.org/W2070493638","https://openalex.org/W2076127024","https://openalex.org/W2110513229","https://openalex.org/W2152141130","https://openalex.org/W2291068538","https://openalex.org/W2295598076","https://openalex.org/W2768348081","https://openalex.org/W2792228752","https://openalex.org/W2898227265","https://openalex.org/W3102476541","https://openalex.org/W6682772626","https://openalex.org/W6745609711","https://openalex.org/W6755712434"],"related_works":["https://openalex.org/W1991715599","https://openalex.org/W4309298396","https://openalex.org/W4248534646","https://openalex.org/W3208169454","https://openalex.org/W3132021049","https://openalex.org/W4298012357","https://openalex.org/W4296079469","https://openalex.org/W1519352203","https://openalex.org/W2074089485","https://openalex.org/W652992540"],"abstract_inverted_index":{"Purpose":[0],"Inspired":[1],"by":[2,21],"the":[3,40,50,54,69,83,91,98,109,117,148,151,154],"basic":[4],"idea":[5],"of":[6,93,150],"gradient":[7,118,168],"boosting,":[8],"this":[9],"study":[10],"aims":[11],"to":[12,49,76,80,116,129,136,145,170],"design":[13,137],"a":[14,27,77,138,162],"novel":[15,139],"multivariate":[16,23],"regression":[17,25,38,60,140,160],"ensemble":[18,173],"algorithm":[19,110,124,141],"RegBoost":[20,111,142,157],"using":[22,53],"linear":[24,37,59,130,159],"as":[26,161],"weak":[28,56,85,95,163],"predictor.":[29,57,86],"Design/methodology/approach":[30],"To":[31,147],"achieve":[32,113],"nonlinearity":[33],"after":[34],"combining":[35],"all":[36,94],"predictors,":[39],"training":[41],"data":[42,73],"is":[43,62,74,90,106,125],"divided":[44],"into":[45],"two":[46,66],"branches":[47],"according":[48],"prediction":[51],"results":[52],"current":[55],"The":[58,87,123],"modeling":[61],"recursively":[63],"executed":[64],"in":[65],"branches.":[67],"In":[68],"test":[70,72],"phase,":[71],"distributed":[75],"specific":[78],"branch":[79],"continue":[81],"with":[82,143,167],"next":[84],"final":[88],"result":[89],"sum":[92],"predictors":[96],"across":[97],"entire":[99],"path.":[100],"Findings":[101],"Through":[102],"comparison":[103],"experiments,":[104],"it":[105],"found":[107],"that":[108],"can":[112],"similar":[114],"performance":[115],"boosted":[119],"decision":[120],"tree":[121],"(GBDT).":[122],"very":[126],"effective":[127],"compared":[128],"regression.":[131],"Originality/value":[132],"This":[133],"paper":[134],"attempts":[135],"reference":[144],"GBDT.":[146],"best":[149],"knowledge,":[152],"for":[153],"first":[155],"time,":[156],"uses":[158],"predictor,":[164],"and":[165],"combine":[166],"boosting":[169],"build":[171],"an":[172],"algorithm.":[174]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-08T08:50:53.379069","created_date":"2025-10-10T00:00:00"}