{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T02:43:50Z","timestamp":1777776230229,"version":"3.51.4"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["61701304"],"award-info":[{"award-number":["61701304"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Sailing Program","award":["17YF1410100"],"award-info":[{"award-number":["17YF1410100"]}]},{"DOI":"10.13039\/501100013285","name":"Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013285","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3070575","type":"journal-article","created":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T02:33:27Z","timestamp":1617935607000},"page":"55999-56011","source":"Crossref","is-referenced-by-count":60,"title":["SDTR: Soft Decision Tree Regressor for Tabular Data"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1951-6678","authenticated-orcid":false,"given":"Haoran","family":"Luo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4307-6334","authenticated-orcid":false,"given":"Fan","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2158-862X","authenticated-orcid":false,"given":"Heng","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6799-5071","authenticated-orcid":false,"given":"Yuqi","family":"Yi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/72.97934"},{"key":"ref38","article-title":"Predicting the future relevance of research institutions&#x2013;the winning solution of the KDD cup 2016","author":"sandulescu","year":"2016","journal-title":"arXiv 1609 02728"},{"key":"ref33","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref32","first-page":"8026","article-title":"Pytorch: An imperative style, high-performance deep learning library","author":"paszke","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.06.005"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.03.045"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.18"},{"key":"ref36","first-page":"2007","article-title":"Generalized boosted models: A guide to the GBM package","volume":"1","author":"ridgeway","year":"2007","journal-title":"Update"},{"key":"ref35","article-title":"Introducing LETOR 4.0 datasets","author":"qin","year":"2013","journal-title":"arXiv 1306 2597"},{"key":"ref34","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref28","first-page":"1","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"nair","year":"2010","journal-title":"Proc ICML"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1137\/1109020"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.05.006"},{"key":"ref2","article-title":"TabNet: Attentive interpretable tabular learning","author":"arik","year":"2019","journal-title":"arXiv 1908 07442"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.12.003"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/s12046-016-0465-z"},{"key":"ref22","author":"lecun","year":"2010","journal-title":"MNIST Handwritten Digit Database"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-30858-6"},{"key":"ref24","first-page":"6166","article-title":"Adaptive neural trees","volume":"97","author":"tanno","year":"2019","journal-title":"Proc 36th Int Conf Mach Learn (ICML)"},{"key":"ref23","first-page":"1","article-title":"Policy-gradient methods for decision trees","author":"l\u00e9on","year":"2016","journal-title":"Proc ESANN"},{"key":"ref26","volume":"821","author":"montgomery","year":"2012","journal-title":"Introduction to Linear Regression Analysis"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"ref50","first-page":"6639","article-title":"CatBoost: Unbiased boosting with categorical features","author":"prokhorenkova","year":"2018","journal-title":"Proc Annu Conf Neural Inf Process Syst (NeurIPS)"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2009.05.016"},{"key":"ref11","author":"dua","year":"2017","journal-title":"UCI Machine Learning Repository"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/34.817409"},{"key":"ref12","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":"Ann Statist"},{"key":"ref13","article-title":"Distilling a neural network into a soft decision tree","author":"frosst","year":"2017","journal-title":"arXiv 1711 09784"},{"key":"ref14","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"2010","journal-title":"Proc 13th Int Conf Artif Intell Statist"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref17","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1994.6.2.181"},{"key":"ref19","first-page":"3146","article-title":"LightGBM: A highly efficient gradient boosting decision tree","author":"ke","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref4","article-title":"The million song dataset","author":"bertin-mahieux","year":"2011","journal-title":"Proc 12th Int Conf Music Inf Retr (ISMIR)"},{"key":"ref3","first-page":"115","article-title":"Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures","author":"bergstra","year":"2013","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref6","author":"breiman","year":"1984","journal-title":"Classification Regression Trees"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-7046-6_19"},{"key":"ref7","first-page":"1","article-title":"Yahoo! learning to rank challenge overview","author":"chapelle","year":"2011","journal-title":"Proc Learn Rank Challenge"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/497"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3124791.3124792"},{"key":"ref45","first-page":"288","article-title":"Locally weighted projection regression: An o(n) algorithm for incremental real time learning in high dimensional space","volume":"1","author":"vijayakumar","year":"2000","journal-title":"Proc 17th Int Conf Mach Learn (ICML)"},{"key":"ref48","first-page":"25","article-title":"Bagging soft decision trees","author":"y\u00edld\u00edz","year":"2016","journal-title":"Proc JMLR Workshop Conf"},{"key":"ref47","article-title":"Deep neural decision trees","author":"yang","year":"2018","journal-title":"arXiv 1806 06988"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3294-8_3"},{"key":"ref41","first-page":"6166","article-title":"Adaptive neural trees","volume":"97","author":"tanno","year":"2019","journal-title":"Proc Mach Learn Res (ICML)"},{"key":"ref44","first-page":"5998","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2010.10.001"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09393908.pdf?arnumber=9393908","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T17:17:15Z","timestamp":1643217435000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9393908\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3070575","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}