{"id":"https://openalex.org/W2986248722","doi":"https://doi.org/10.1109/iisa.2019.8900737","title":"Self-trained eXtreme Gradient Boosting Trees","display_name":"Self-trained eXtreme Gradient Boosting Trees","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2986248722","doi":"https://doi.org/10.1109/iisa.2019.8900737","mag":"2986248722"},"language":"en","primary_location":{"id":"doi:10.1109/iisa.2019.8900737","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iisa.2019.8900737","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5061070955","display_name":"Nikos Fazakis","orcid":"https://orcid.org/0000-0001-7687-2380"},"institutions":[{"id":"https://openalex.org/I174878644","display_name":"University of Patras","ror":"https://ror.org/017wvtq80","country_code":"GR","type":"education","lineage":["https://openalex.org/I174878644"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Nikos Fazakis","raw_affiliation_strings":["Department of Electrical and Computer, Engineering University of Patras, Patras, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer, Engineering University of Patras, Patras, Greece","institution_ids":["https://openalex.org/I174878644"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027660188","display_name":"Georgios Kostopoulos","orcid":"https://orcid.org/0000-0002-7374-0099"},"institutions":[{"id":"https://openalex.org/I174878644","display_name":"University of Patras","ror":"https://ror.org/017wvtq80","country_code":"GR","type":"education","lineage":["https://openalex.org/I174878644"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Georgios Kostopoulos","raw_affiliation_strings":["Department of Mathematics, University of Patras, Patras, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Patras, Patras, Greece","institution_ids":["https://openalex.org/I174878644"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073119708","display_name":"Stamatis Karlos","orcid":"https://orcid.org/0000-0002-5307-6186"},"institutions":[{"id":"https://openalex.org/I174878644","display_name":"University of Patras","ror":"https://ror.org/017wvtq80","country_code":"GR","type":"education","lineage":["https://openalex.org/I174878644"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Stamatis Karlos","raw_affiliation_strings":["Department of Mathematics, University of Patras, Patras, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Patras, Patras, Greece","institution_ids":["https://openalex.org/I174878644"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066370772","display_name":"Sotiris Kotsiantis","orcid":"https://orcid.org/0000-0002-2247-3082"},"institutions":[{"id":"https://openalex.org/I174878644","display_name":"University of Patras","ror":"https://ror.org/017wvtq80","country_code":"GR","type":"education","lineage":["https://openalex.org/I174878644"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Sotiris Kotsiantis","raw_affiliation_strings":["Department of Mathematics, University of Patras, Patras, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Patras, Patras, Greece","institution_ids":["https://openalex.org/I174878644"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012058127","display_name":"Kyriakos Sgarbas","orcid":"https://orcid.org/0000-0002-1797-1343"},"institutions":[{"id":"https://openalex.org/I174878644","display_name":"University of Patras","ror":"https://ror.org/017wvtq80","country_code":"GR","type":"education","lineage":["https://openalex.org/I174878644"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Kyriakos Sgarbas","raw_affiliation_strings":["Department of Electrical and Computer, Engineering University of Patras, Patras, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer, Engineering University of Patras, Patras, Greece","institution_ids":["https://openalex.org/I174878644"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I174878644"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9988999962806702,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9988999962806702,"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/T10057","display_name":"Face and Expression Recognition","score":0.9944000244140625,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9793000221252441,"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/boosting","display_name":"Boosting (machine learning)","score":0.822504997253418},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.7329617738723755},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.720943808555603},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6762101650238037},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6481655836105347},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5936612486839294},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.527962327003479},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4622243642807007},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.44589096307754517},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4457646608352661},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3685981035232544},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17327454686164856},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.14135336875915527},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08830085396766663}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.822504997253418},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.7329617738723755},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.720943808555603},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6762101650238037},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6481655836105347},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5936612486839294},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.527962327003479},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4622243642807007},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.44589096307754517},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4457646608352661},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3685981035232544},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17327454686164856},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.14135336875915527},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08830085396766663},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iisa.2019.8900737","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iisa.2019.8900737","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W189198445","https://openalex.org/W1479758384","https://openalex.org/W1974758710","https://openalex.org/W1990334093","https://openalex.org/W2036893104","https://openalex.org/W2048679005","https://openalex.org/W2065181790","https://openalex.org/W2092481996","https://openalex.org/W2094490861","https://openalex.org/W2098708659","https://openalex.org/W2106401878","https://openalex.org/W2125055259","https://openalex.org/W2128518360","https://openalex.org/W2133556223","https://openalex.org/W2182722412","https://openalex.org/W2295598076","https://openalex.org/W2487770199","https://openalex.org/W2746791238","https://openalex.org/W2808462996","https://openalex.org/W2862362254","https://openalex.org/W2912934387","https://openalex.org/W3040887816","https://openalex.org/W3102476541","https://openalex.org/W4212883601","https://openalex.org/W4255240758","https://openalex.org/W4399647672","https://openalex.org/W6607691338","https://openalex.org/W6670404919","https://openalex.org/W6753628185","https://openalex.org/W6869608176"],"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/W4310224730","https://openalex.org/W2766514146","https://openalex.org/W4289703016","https://openalex.org/W2885516856","https://openalex.org/W3094138326"],"abstract_inverted_index":{"Semi-Supervised":[0],"Learning":[1],"(SSL)":[2],"is":[3,31,71,106],"an":[4,72,110],"ever-growing":[5],"research":[6],"area":[7],"offering":[8],"a":[9,32,47,77,88,127,134],"powerful":[10],"set":[11],"of":[12,50,63,94,103,120,143,153],"methods,":[13,160],"either":[14],"single":[15],"or":[16],"multi-view,":[17],"for":[18,41,60,80,114],"exploiting":[19],"both":[20,81],"labeled":[21],"and":[22,83,137],"unlabeled":[23],"instances":[24],"in":[25,46,91,126,130],"the":[26,58,61,92,100,118,151,154,165],"most":[27],"effective":[28],"manner.":[29],"Self-training":[30],"representative":[33,158],"SSL":[34],"algorithm":[35,79,113],"which":[36,86],"has":[37,55],"been":[38],"efficiently":[39],"implemented":[40],"solving":[42],"several":[43,64],"classification":[44,82,115,139],"problems":[45],"wide":[48],"range":[49],"scientific":[51],"fields.":[52],"Moreover,":[53],"self-training":[54,112],"served":[56],"as":[57,161],"base":[59],"development":[62],"self-labeled":[65,128],"methods.":[66],"In":[67,97],"addition,":[68],"gradient":[69],"boosting":[70,78],"advanced":[73],"machine":[74],"learning":[75],"technique,":[76],"regression":[84],"problems,":[85],"produces":[87],"predictive":[89],"model":[90],"form":[93],"decision":[95],"trees.":[96],"this":[98,104],"context,":[99],"principal":[101],"objective":[102],"paper":[105],"to":[107,132],"put":[108],"forward":[109],"improved":[111],"tasks":[116],"utilizing":[117],"efficacy":[119],"eXtreme":[121],"Gradient":[122],"Boosting":[123],"(XGBoost)":[124],"trees":[125],"scheme":[129],"order":[131],"build":[133],"highly":[135],"accurate":[136],"robust":[138],"model.":[140],"A":[141],"number":[142],"experiments":[144],"on":[145],"benchmark":[146],"datasets":[147],"were":[148],"executed":[149],"demonstrating":[150],"superiority":[152],"proposed":[155],"method":[156],"over":[157],"semi-supervised":[159],"statistically":[162],"verified":[163],"by":[164],"Friedman":[166],"non-parametric":[167],"test.":[168]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
