{"id":"https://openalex.org/W2869198707","doi":"https://doi.org/10.1145/3200947.3201060","title":"Sports & Nutrition Data Science using Gradient Boosting Machines","display_name":"Sports & Nutrition Data Science using Gradient Boosting Machines","publication_year":2018,"publication_date":"2018-07-06","ids":{"openalex":"https://openalex.org/W2869198707","doi":"https://doi.org/10.1145/3200947.3201060","mag":"2869198707"},"language":"en","primary_location":{"id":"doi:10.1145/3200947.3201060","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3200947.3201060","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th Hellenic Conference on Artificial Intelligence","raw_type":"proceedings-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/A5021751164","display_name":"Antonios Pantazopoulos","orcid":null},"institutions":[{"id":"https://openalex.org/I98805295","display_name":"University of the Aegean","ror":"https://ror.org/03zsp3p94","country_code":"GR","type":"education","lineage":["https://openalex.org/I98805295"]}],"countries":["GR"],"is_corresponding":true,"raw_author_name":"Antonios Pantazopoulos","raw_affiliation_strings":["Artificial Intelligence Group, Department of Information and Communication Systems Engineering, University of the Aegean, Samos, Greece"],"affiliations":[{"raw_affiliation_string":"Artificial Intelligence Group, Department of Information and Communication Systems Engineering, University of the Aegean, Samos, Greece","institution_ids":["https://openalex.org/I98805295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012359011","display_name":"Manolis \u039caragoudakis","orcid":"https://orcid.org/0000-0001-7701-0141"},"institutions":[{"id":"https://openalex.org/I98805295","display_name":"University of the Aegean","ror":"https://ror.org/03zsp3p94","country_code":"GR","type":"education","lineage":["https://openalex.org/I98805295"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Manolis Maragoudakis","raw_affiliation_strings":["Artificial Intelligence Group, Department of Information and Communication Systems Engineering, University of the Aegean, Samos, Greece"],"affiliations":[{"raw_affiliation_string":"Artificial Intelligence Group, Department of Information and Communication Systems Engineering, University of the Aegean, Samos, Greece","institution_ids":["https://openalex.org/I98805295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5021751164"],"corresponding_institution_ids":["https://openalex.org/I98805295"],"apc_list":null,"apc_paid":null,"fwci":1.8136,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.88776095,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9538000226020813,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9538000226020813,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9510999917984009,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9329000115394592,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/boosting","display_name":"Boosting (machine learning)","score":0.71851646900177},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6135294437408447},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.5318474769592285},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.38222748041152954},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3766034245491028},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34711793065071106},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.07190901041030884}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.71851646900177},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6135294437408447},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.5318474769592285},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.38222748041152954},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3766034245491028},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34711793065071106},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.07190901041030884}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3200947.3201060","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3200947.3201060","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th Hellenic Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2025541693","https://openalex.org/W2074204403","https://openalex.org/W2076063813","https://openalex.org/W2088794999","https://openalex.org/W2092093206","https://openalex.org/W2112126701","https://openalex.org/W2122858723","https://openalex.org/W2130771251","https://openalex.org/W2131828349","https://openalex.org/W2137226992","https://openalex.org/W2147697413","https://openalex.org/W2151509918","https://openalex.org/W2295598076","https://openalex.org/W2607650000","https://openalex.org/W2620751586","https://openalex.org/W2626760611","https://openalex.org/W2760462550","https://openalex.org/W2767171489","https://openalex.org/W2919115771","https://openalex.org/W2965036738","https://openalex.org/W3102476541","https://openalex.org/W4250857377","https://openalex.org/W6678395704","https://openalex.org/W6680532697"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W2766514146","https://openalex.org/W2885516856","https://openalex.org/W4289703016","https://openalex.org/W3094138326","https://openalex.org/W4310224730"],"abstract_inverted_index":{"Throughout1":[0],"recent":[1,105],"years,":[2],"a":[3,67,147],"significant":[4,313],"amount":[5,69],"of":[6,60,66,70,117,126,146,164,173,199,236,298,332,337],"research":[7],"has":[8],"dealt":[9],"with":[10,155],"how":[11,118],"humans":[12],"could":[13,141,320],"beat":[14],"time.":[15],"According":[16],"to":[17,40,63,75,96,111,114,194,240,284,303,310],"the":[18,21,47,64,94,115,124,143,171,174,196,237,242,275,305,315,330],"extracted":[19],"outcome,":[20],"main":[22],"weapons":[23],"in":[24],"this":[25],"battle":[26],"are":[27,300,308],"balanced":[28],"nutrition":[29,131,160,226],"and":[30,50,87,122,133,159,168,181,206,212,252,265,335,344],"systematic":[31],"exercise.":[32],"Outdoor":[33],"running,":[34],"ranging":[35],"from":[36,128,162],"short":[37],"distances":[38],"up":[39],"even":[41,51],"marathons,":[42],"is":[43,109,210,280],"gaining":[44],"popularity,":[45],"boosting":[46],"clothing,":[48],"shoes":[49,86],"gadget":[52],"industry":[53],"as":[54,256],"well.":[55],"The":[56,104,151,208,230],"current":[57,152,272,342],"rapid":[58],"development":[59],"technology":[61],"leads":[62],"supply":[65],"vast":[68],"information":[71],"that":[72,140,221,307],"enables":[73],"us":[74],"properly":[76],"process":[77],"valuable":[78],"knowledge.":[79],"Smart":[80],"watches,":[81],"wristbands,":[82],"mobile":[83],"phone":[84],"applications,":[85],"clothes":[88],"sensors,":[89],"etc.":[90],"have":[91],"provided":[92],"practitioners":[93],"tools":[95],"collect":[97],"numerous":[98],"data":[99,107,127,187],"regarding":[100],"their":[101,341],"fitness":[102,134,158,224,343],"levels.":[103,346],"challenge":[106],"mining":[108],"about":[110],"serve,":[112],"lies":[113],"fact":[116],"one":[119],"can":[120],"combine":[121],"analyze":[123],"abundance":[125],"daily":[129],"activities,":[130],"habits":[132,227],"activities":[135],"into":[136],"one,":[137],"unified":[138],"model":[139],"explain":[142],"underlying":[144],"patterns":[145],"subject's":[148],"physical":[149],"capacity.":[150],"work":[153],"deals":[154],"collecting":[156],"heterogeneous":[157],"facts":[161],"students":[163],"various":[165],"athletic":[166],"background":[167],"skills":[169],"within":[170],"University":[172],"Aegean":[175],"campus,":[176],"using":[177,214],"hi-tech":[178],"GPS":[179],"smartwatches":[180],"calories":[182],"monitoring":[183],"smartwatch":[184],"applications.":[185],"Upon":[186],"collection,":[188],"we":[189],"apply":[190],"Gradient":[191],"Boosting":[192],"Machines":[193],"forecast":[195],"finishing":[197,243],"time":[198,244],"two":[200],"different":[201,215],"running":[202],"tasks,":[203],"namely":[204],"800m":[205],"5000m.":[207],"algorithm":[209],"trained":[211],"evaluated":[213],"attribute":[216],"sets,":[217],"focusing":[218],"on":[219,271,274],"features":[220],"describe":[222],"either":[223],"skills,":[225],"or":[228,326],"both.":[229],"results":[231,289],"demonstrate":[232],"an":[233],"impressive":[234],"ability":[235],"aforementioned":[238],"method":[239],"predict":[241],"which":[245],"surpasses":[246],"many":[247],"other":[248],"Machine":[249],"Learning":[250],"algorithms":[251],"traditional":[253],"models":[254],"such":[255],"Support":[257],"Vector":[258],"Machines,":[259],"Random":[260],"Forests,":[261],"Deep":[262],"Neural":[263],"Networks":[264],"Linear":[266,285],"Regression.":[267],"Note":[268],"that,":[269],"based":[270],"literature":[273],"task":[276],"at":[277],"hand,":[278],"prediction":[279],"almost":[281],"exclusively":[282],"relied":[283],"Regression":[286],"models,":[287],"mentioning":[288],"far":[290],"worse":[291],"than":[292],"GBM.":[293],"Finally,":[294],"feature":[295],"importance":[296],"functionalities":[297],"GBM":[299],"also":[301],"exploited":[302],"reveal":[304],"factors":[306,319],"considered":[309],"be":[311,321],"most":[312],"during":[314],"forecasting":[316],"process.":[317],"Such":[318],"further":[322],"utilized":[323],"by":[324],"professional":[325,333],"amateur":[327],"athletes":[328],"(with":[329],"assistant":[331],"trainers":[334],"experts":[336],"course),":[338],"for":[339],"improving":[340],"health":[345]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}