{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T01:32:21Z","timestamp":1772501541257,"version":"3.50.1"},"reference-count":21,"publisher":"Tsinghua University Press","issue":"1","license":[{"start":{"date-parts":[[2020,1,31]],"date-time":"2020-01-31T00:00:00Z","timestamp":1580428800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCS"],"published-print":{"date-parts":[[2020,1,31]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Inspired by the basic idea of gradient boosting, this study aims to design a novel multivariate regression ensemble algorithm RegBoost by using multivariate linear regression as a weak predictor.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>To achieve nonlinearity after combining all linear regression predictors, the training data is divided into two branches according to the prediction results using the current weak predictor. The linear regression modeling is recursively executed in two branches. In the test phase, test data is distributed to a specific branch to continue with the next weak predictor. The final result is the sum of all weak predictors across the entire path.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>Through comparison experiments, it is found that the algorithm RegBoost can achieve similar performance to the gradient boosted decision tree (GBDT). The algorithm is very effective compared to linear regression.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This paper attempts to design a novel regression algorithm RegBoost with reference to GBDT. To the best of the knowledge, for the first time, RegBoost uses linear regression as a weak predictor, and combine with gradient boosting to build an ensemble algorithm.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ijcs-10-2019-0029","type":"journal-article","created":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T16:13:52Z","timestamp":1580746432000},"page":"60-72","source":"Crossref","is-referenced-by-count":16,"title":["RegBoost: a gradient boosted multivariate regression algorithm"],"prefix":"10.26599","volume":"4","author":[{"given":"Wen","family":"Li","sequence":"first","affiliation":[]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Wenjun","family":"Huo","sequence":"additional","affiliation":[]}],"member":"11138","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-9473(01)00065-2"},{"key":"ref11","first-page":"32","article-title":"The analysis and selection of variables in linear regression","author":"hocking","year":"1976","journal-title":"Biometrics"},{"key":"ref12","first-page":"1","article-title":"Gradient boosting learning of hidden Markov models","author":"hu","year":"2006","journal-title":"2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings"},{"key":"ref13","first-page":"13","article-title":"Local and global learning methods for predicting power of a combined gas and steam turbine","author":"kaya","year":"2012","journal-title":"Proceedings of the international conference on emerging trends in computer and electronics engineering icetcee"},{"key":"ref14","article-title":"LightGBM: a highly efficient gradient boosting decision tree","author":"ke","year":"2017","journal-title":"31st Conference on Neural Information Processing Systems"},{"key":"ref15","article-title":"Boosting Soft-Margin SVM with feature selection for pedestrian detection","author":"kenji","year":"2005","journal-title":"International Workshop on Multiple Classifier Systems"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP.2017.8309190"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s10651-007-0043-y"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2014.02.027"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3354\/cr030079"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2006.412"},{"key":"ref3","year":"2019"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref5","author":"breiman","year":"1997","journal-title":"The Edge"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-014-9474-0"},{"key":"ref7","article-title":"CatBoost: gradient Boosting with Categorical Features Support","author":"dorogush","year":"0","journal-title":"NIPS ML Systems Workshop"},{"key":"ref2","year":"2019"},{"key":"ref1","year":"2019"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: a gradient boosting machine","volume":"29","author":"friedman","year":"2001","journal-title":"The Annals of Statistics"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1080\/13658810500286976"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2015.2488681"}],"container-title":["International Journal of Crowd Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJCS-10-2019-0029\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJCS-10-2019-0029\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T14:09:48Z","timestamp":1657634988000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJCS-10-2019-0029\/full\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,31]]},"references-count":21,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,1,31]]}},"alternative-id":["10.1108\/IJCS-10-2019-0029"],"URL":"https:\/\/doi.org\/10.1108\/ijcs-10-2019-0029","relation":{},"ISSN":["2398-7294","2398-7294"],"issn-type":[{"value":"2398-7294","type":"print"},{"value":"2398-7294","type":"print"}],"subject":[],"published":{"date-parts":[[2020,1,31]]}}}