{"id":"https://openalex.org/W4224131903","doi":"https://doi.org/10.3390/bdcc6020041","title":"Revisiting Gradient Boosting-Based Approaches for Learning Imbalanced Data: A Case of Anomaly Detection on Power Grids","display_name":"Revisiting Gradient Boosting-Based Approaches for Learning Imbalanced Data: A Case of Anomaly Detection on Power Grids","publication_year":2022,"publication_date":"2022-04-16","ids":{"openalex":"https://openalex.org/W4224131903","doi":"https://doi.org/10.3390/bdcc6020041"},"language":"en","primary_location":{"id":"doi:10.3390/bdcc6020041","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc6020041","pdf_url":"https://www.mdpi.com/2504-2289/6/2/41/pdf?version=1650083585","source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data and Cognitive Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-2289/6/2/41/pdf?version=1650083585","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071291178","display_name":"Maya Hilda Lestari Louk","orcid":"https://orcid.org/0000-0001-8274-0990"},"institutions":[{"id":"https://openalex.org/I16413167","display_name":"University of Surabaya","ror":"https://ror.org/013314927","country_code":"ID","type":"education","lineage":["https://openalex.org/I16413167"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Maya Hilda Lestari Louk","raw_affiliation_strings":["Department of Informatics Engineering, University of Surabaya, Surabaya 60293, Indonesia"],"raw_orcid":"https://orcid.org/0000-0001-8274-0990","affiliations":[{"raw_affiliation_string":"Department of Informatics Engineering, University of Surabaya, Surabaya 60293, Indonesia","institution_ids":["https://openalex.org/I16413167"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014306565","display_name":"Bayu Adhi Tama","orcid":"https://orcid.org/0000-0002-1821-6438"},"institutions":[{"id":"https://openalex.org/I4210104335","display_name":"Institute for Basic Science","ror":"https://ror.org/00y0zf565","country_code":"KR","type":"facility","lineage":["https://openalex.org/I4210104335"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Bayu Adhi Tama","raw_affiliation_strings":["Data Science Group, Institute for Basic Science (IBS), Daejeon 34141, Korea"],"raw_orcid":"https://orcid.org/0000-0002-1821-6438","affiliations":[{"raw_affiliation_string":"Data Science Group, Institute for Basic Science (IBS), Daejeon 34141, Korea","institution_ids":["https://openalex.org/I4210104335"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5014306565"],"corresponding_institution_ids":["https://openalex.org/I4210104335"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":3.0518,"has_fulltext":true,"cited_by_count":23,"citation_normalized_percentile":{"value":0.92305052,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"6","issue":"2","first_page":"41","last_page":"41"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9997000098228455,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9997000098228455,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.9979000091552734,"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":0.8307574987411499},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.8060070276260376},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5988177061080933},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5639326572418213},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5526875257492065},{"id":"https://openalex.org/keywords/matthews-correlation-coefficient","display_name":"Matthews correlation coefficient","score":0.5364248156547546},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.5346919894218445},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5307731032371521},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5104914903640747},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.5080602169036865},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.25805729627609253}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.8307574987411499},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.8060070276260376},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5988177061080933},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5639326572418213},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5526875257492065},{"id":"https://openalex.org/C164085508","wikidata":"https://www.wikidata.org/wiki/Q4811327","display_name":"Matthews correlation coefficient","level":3,"score":0.5364248156547546},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.5346919894218445},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5307731032371521},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5104914903640747},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.5080602169036865},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.25805729627609253},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/bdcc6020041","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc6020041","pdf_url":"https://www.mdpi.com/2504-2289/6/2/41/pdf?version=1650083585","source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data and Cognitive Computing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:30862cd694d34c01ad0697fad80adba6","is_oa":true,"landing_page_url":"https://doaj.org/article/30862cd694d34c01ad0697fad80adba6","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Big Data and Cognitive Computing, Vol 6, Iss 2, p 41 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2504-2289/6/2/41/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/bdcc6020041","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Big Data and Cognitive Computing; Volume 6; Issue 2; Pages: 41","raw_type":"Text"},{"id":"pmh:oai:repository.ubaya.ac.id:41773","is_oa":true,"landing_page_url":"https://www.mdpi.com/2504-2289/6/2/41","pdf_url":null,"source":{"id":"https://openalex.org/S4306402324","display_name":"Ubaya Repository (University of Surabaya)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I16413167","host_organization_name":"University of Surabaya","host_organization_lineage":["https://openalex.org/I16413167"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":{"id":"doi:10.3390/bdcc6020041","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc6020041","pdf_url":"https://www.mdpi.com/2504-2289/6/2/41/pdf?version=1650083585","source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data and Cognitive Computing","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.4300000071525574,"display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G5885902318","display_name":null,"funder_award_id":"IBS-R029-C2","funder_id":"https://openalex.org/F4320326441","funder_display_name":"Institute for Basic Science"},{"id":"https://openalex.org/G8896287674","display_name":null,"funder_award_id":"IBS-R029-C2-001","funder_id":"https://openalex.org/F4320326441","funder_display_name":"Institute for Basic Science"}],"funders":[{"id":"https://openalex.org/F4320326441","display_name":"Institute for Basic Science","ror":"https://ror.org/00y0zf565"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4224131903.pdf","grobid_xml":"https://content.openalex.org/works/W4224131903.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W1565746575","https://openalex.org/W1678356000","https://openalex.org/W1977555035","https://openalex.org/W1991510770","https://openalex.org/W2016944307","https://openalex.org/W2020524857","https://openalex.org/W2049017883","https://openalex.org/W2087240369","https://openalex.org/W2088068024","https://openalex.org/W2097998348","https://openalex.org/W2295598076","https://openalex.org/W2768348081","https://openalex.org/W2787040861","https://openalex.org/W2798476254","https://openalex.org/W2801513872","https://openalex.org/W2964216374","https://openalex.org/W2989699619","https://openalex.org/W2996221774","https://openalex.org/W2996717911","https://openalex.org/W2999309192","https://openalex.org/W3093293285","https://openalex.org/W3093981769","https://openalex.org/W3115025749","https://openalex.org/W3126232929","https://openalex.org/W3162387616","https://openalex.org/W3171389320","https://openalex.org/W3184553632","https://openalex.org/W4200525072","https://openalex.org/W4245055982","https://openalex.org/W6674385629"],"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":{"Gradient":[0],"boosting":[1,54,59],"ensembles":[2],"have":[3],"been":[4],"used":[5],"in":[6,38,99,109],"the":[7,36,39,46,67,75,82,126,130,146],"cyber-security":[8],"area":[9,80],"for":[10,18],"many":[11],"years;":[12],"nonetheless,":[13],"their":[14],"efficacy":[15],"and":[16,62,88,141],"accuracy":[17],"intrusion":[19],"detection":[20,98,108],"systems":[21],"(IDSs)":[22],"remain":[23],"questionable,":[24],"particularly":[25],"when":[26],"dealing":[27],"with":[28],"problems":[29],"involving":[30],"imbalanced":[31,71],"data.":[32],"This":[33,64],"article":[34,92],"fills":[35],"void":[37],"existing":[40],"body":[41],"of":[42,48,69,96,125,129],"knowledge":[43],"by":[44],"evaluating":[45],"performance":[47,68,139],"gradient":[49,53,58],"boosting-based":[50],"ensembles,":[51],"including":[52],"machine":[55],"(GBM),":[56],"extreme":[57],"(XGBoost),":[60],"LightGBM,":[61],"CatBoost.":[63],"paper":[65],"assesses":[66],"various":[70],"data":[72,131,147],"sets":[73],"using":[74],"Matthew":[76],"correlation":[77],"coefficient":[78],"(MCC),":[79],"under":[81],"receiver":[83],"operating":[84],"characteristic":[85],"curve":[86],"(AUC),":[87],"F1":[89],"metrics.":[90],"The":[91,115],"discusses":[93],"an":[94,100],"example":[95],"anomaly":[97],"industrial":[101],"control":[102],"network":[103],"and,":[104],"more":[105,143],"specifically,":[106],"threat":[107],"a":[110,136],"cyber-physical":[111],"smart":[112],"power":[113],"grid.":[114],"tests\u2019":[116],"results":[117],"indicate":[118],"that":[119],"CatBoost":[120],"surpassed":[121],"its":[122],"competitors,":[123],"regardless":[124],"imbalance":[127],"ratio":[128],"sets.":[132,148],"Moreover,":[133],"LightGBM":[134],"showed":[135],"much":[137],"lower":[138],"value":[140],"had":[142],"variability":[144],"across":[145]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
