{"id":"https://openalex.org/W3212747230","doi":"https://doi.org/10.1109/tkde.2021.3126615","title":"Single-Index Model Tree","display_name":"Single-Index Model Tree","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3212747230","doi":"https://doi.org/10.1109/tkde.2021.3126615","mag":"3212747230"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2021.3126615","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2021.3126615","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001331464","display_name":"Agus Sudjianto","orcid":"https://orcid.org/0000-0003-0846-9746"},"institutions":[{"id":"https://openalex.org/I166794780","display_name":"Wells Fargo (United States)","ror":"https://ror.org/037r2ff59","country_code":"US","type":"company","lineage":["https://openalex.org/I166794780"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Agus Sudjianto","raw_affiliation_strings":["Corporate Model Risk, Wells Fargo, Charlotte, North Carolina, United States, (e-mail: Agus.Sudjianto@wellsfargo.com)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Corporate Model Risk, Wells Fargo, Charlotte, North Carolina, United States, (e-mail: Agus.Sudjianto@wellsfargo.com)","institution_ids":["https://openalex.org/I166794780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065081397","display_name":"Zebin Yang","orcid":"https://orcid.org/0000-0001-5683-7502"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]},{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Zebin Yang","raw_affiliation_strings":["Department of Statistics and Actuarial Science, University of Hong Kong, 25809 Hong Kong, Hong Kong, Hong Kong, (e-mail: yangzb2010@connect.hku.hk)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Actuarial Science, University of Hong Kong, 25809 Hong Kong, Hong Kong, Hong Kong, (e-mail: yangzb2010@connect.hku.hk)","institution_ids":["https://openalex.org/I200769079","https://openalex.org/I889458895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101664060","display_name":"Aijun Zhang","orcid":"https://orcid.org/0000-0001-9729-9018"},"institutions":[{"id":"https://openalex.org/I166794780","display_name":"Wells Fargo (United States)","ror":"https://ror.org/037r2ff59","country_code":"US","type":"company","lineage":["https://openalex.org/I166794780"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aijun Zhang","raw_affiliation_strings":["Corporate Model Risk, Wells Fargo, Charlotte, North Carolina, United States, (e-mail: Aijun.Zhang@wellsfargo.com)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Corporate Model Risk, Wells Fargo, Charlotte, North Carolina, United States, (e-mail: Aijun.Zhang@wellsfargo.com)","institution_ids":["https://openalex.org/I166794780"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.273,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.64000913,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9986000061035156,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9986000061035156,"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":0.9936000108718872,"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/T10320","display_name":"Neural Networks and Applications","score":0.9896000027656555,"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/computer-science","display_name":"Computer science","score":0.8032315373420715},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.643348753452301},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5263910293579102},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4904634654521942},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4595794081687927},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4589157998561859},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.45887649059295654},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3529282212257385},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16561922430992126}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8032315373420715},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.643348753452301},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5263910293579102},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4904634654521942},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4595794081687927},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4589157998561859},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.45887649059295654},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3529282212257385},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16561922430992126},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2021.3126615","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2021.3126615","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4399999976158142,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W564695097","https://openalex.org/W1487825358","https://openalex.org/W1525765074","https://openalex.org/W1594031697","https://openalex.org/W1678356000","https://openalex.org/W1901355268","https://openalex.org/W1973153036","https://openalex.org/W1974806455","https://openalex.org/W1981039744","https://openalex.org/W1990351156","https://openalex.org/W2007676551","https://openalex.org/W2023320048","https://openalex.org/W2024046085","https://openalex.org/W2045803758","https://openalex.org/W2098749003","https://openalex.org/W2102201073","https://openalex.org/W2121716262","https://openalex.org/W2136175429","https://openalex.org/W2163490846","https://openalex.org/W2171050905","https://openalex.org/W2188236168","https://openalex.org/W2282821441","https://openalex.org/W2396610607","https://openalex.org/W2741071393","https://openalex.org/W2806087506","https://openalex.org/W2806874342","https://openalex.org/W2874218187","https://openalex.org/W2884924868","https://openalex.org/W2897873189","https://openalex.org/W2910705748","https://openalex.org/W2945976633","https://openalex.org/W2962862931","https://openalex.org/W2973125071","https://openalex.org/W2979156612","https://openalex.org/W2999615587","https://openalex.org/W3042224785","https://openalex.org/W3045825034","https://openalex.org/W3046005137","https://openalex.org/W3100189218","https://openalex.org/W3117140277","https://openalex.org/W3153738260","https://openalex.org/W3186769346","https://openalex.org/W4240861491","https://openalex.org/W4244575692","https://openalex.org/W4300782356","https://openalex.org/W4402843978","https://openalex.org/W6633301734","https://openalex.org/W6742026021","https://openalex.org/W6751804815","https://openalex.org/W6752336104","https://openalex.org/W6753898783","https://openalex.org/W6755849684"],"related_works":["https://openalex.org/W2341492732","https://openalex.org/W3187193180","https://openalex.org/W106542691","https://openalex.org/W4287027380","https://openalex.org/W1699080303","https://openalex.org/W4297799326","https://openalex.org/W3116064965","https://openalex.org/W3193760048","https://openalex.org/W4285822516","https://openalex.org/W2551200859"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"a":[3,24,30,50,83,99,148],"novel":[4],"single-index":[5,25],"model":[6,26,164],"tree":[7,144],"(SIMTree)":[8],"is":[9,21,29,103,127,153,175],"proposed.":[10],"It":[11],"adopts":[12],"the":[13,112,119,132,143,163],"recursive":[14],"partitioning":[15],"strategy":[16],"and":[17,111,138,178],"each":[18,69],"data":[19,71],"segment":[20,72],"modeled":[22],"by":[23,131],"(SIM),":[27],"which":[28],"flexible":[31],"extension":[32],"of":[33,114,135],"linear":[34],"regression":[35,170],"with":[36,48,155],"non-parametric":[37],"link":[38],"functions.":[39],"The":[40],"proposed":[41,128],"SIMTree":[42,84,174],"has":[43],"2":[44],"major":[45],"advantages:":[46],"a)":[47],"only":[49],"few":[51],"leaf":[52,108],"nodes,":[53],"it":[54],"can":[55,85,160],"achieve":[56],"competitive":[57],"predictive":[58],"performance":[59],"compared":[60],"to":[61,80],"complicated":[62],"black-box":[63],"models;":[64],"b)":[65],"SIMs":[66,110],"fitted":[67],"on":[68,168],"local":[70],"are":[73],"intrinsically":[74],"interpretable.":[75],"However,":[76],"using":[77],"conventional":[78],"techniques":[79],"build":[81],"such":[82,98],"be":[86],"extremely":[87],"time-consuming.":[88],"SIM":[89],"estimation":[90,101],"typically":[91],"involves":[92],"iterative":[93],"optimization":[94],"via":[95],"Newton-type":[96],"algorithms;":[97],"resource-intensive":[100],"procedure":[102],"repeatedly":[104],"used":[105],"for":[106],"fitting":[107],"node":[109],"search":[113],"optimal":[115],"splits.":[116],"To":[117],"make":[118],"computation":[120],"burden":[121],"affordable,":[122],"an":[123,176],"effective":[124],"training":[125],"algorithm":[126],"as":[129],"enabled":[130],"efficient":[133],"utilization":[134],"Stein's":[136],"lemma":[137],"several":[139],"accelerating":[140],"strategies":[141],"in":[142],"construction":[145],"algorithm.":[146],"Moreover,":[147],"new":[149],"Python":[150],"package":[151],"simtree":[152],"developed":[154],"elegant":[156],"visualization":[157],"modules":[158],"that":[159,173],"further":[161],"facilitate":[162],"interpretation.":[165],"Numerical":[166],"results":[167],"extensive":[169],"datasets":[171],"show":[172],"accurate":[177],"interpretable":[179],"machine":[180],"learning":[181],"model.":[182]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
