{"id":"https://openalex.org/W3099088926","doi":"https://doi.org/10.1108/dta-04-2020-0093","title":"Gradient boosting learning for fraudulent publisher detection in online advertising","display_name":"Gradient boosting learning for fraudulent publisher detection in online advertising","publication_year":2020,"publication_date":"2020-11-13","ids":{"openalex":"https://openalex.org/W3099088926","doi":"https://doi.org/10.1108/dta-04-2020-0093","mag":"3099088926"},"language":"en","primary_location":{"id":"doi:10.1108/dta-04-2020-0093","is_oa":false,"landing_page_url":"https://doi.org/10.1108/dta-04-2020-0093","pdf_url":null,"source":{"id":"https://openalex.org/S4210171756","display_name":"Data Technologies and Applications","issn_l":"2514-9288","issn":["2514-9288","2514-9318"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Technologies and Applications","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/A5017874265","display_name":"Deepti Sisodia","orcid":"https://orcid.org/0000-0002-0338-1270"},"institutions":[{"id":"https://openalex.org/I38335241","display_name":"National Institute of Technology Raipur","ror":"https://ror.org/02y553197","country_code":"IN","type":"education","lineage":["https://openalex.org/I38335241"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Deepti Sisodia","raw_affiliation_strings":["Department of Computer Science and Engineering, National Institute of Technology, Raipur, India"],"raw_orcid":"https://orcid.org/0000-0002-0338-1270","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, National Institute of Technology, Raipur, India","institution_ids":["https://openalex.org/I38335241"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013636671","display_name":"Dilip Singh Sisodia","orcid":"https://orcid.org/0000-0001-9845-290X"},"institutions":[{"id":"https://openalex.org/I38335241","display_name":"National Institute of Technology Raipur","ror":"https://ror.org/02y553197","country_code":"IN","type":"education","lineage":["https://openalex.org/I38335241"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Dilip Singh Sisodia","raw_affiliation_strings":["Department of Computer Science and Engineering, National Institute of Technology, Raipur, India"],"raw_orcid":"https://orcid.org/0000-0001-9845-290X","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, National Institute of Technology, Raipur, India","institution_ids":["https://openalex.org/I38335241"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I38335241"],"apc_list":null,"apc_paid":null,"fwci":2.3024,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.90852027,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"55","issue":"2","first_page":"216","last_page":"232"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9998999834060669,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9998999834060669,"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/T11644","display_name":"Spam and Phishing Detection","score":0.9994999766349792,"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"}},{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9965999722480774,"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":0.7916964292526245},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7519649267196655},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.731417715549469},{"id":"https://openalex.org/keywords/cardinality","display_name":"Cardinality (data modeling)","score":0.6165902614593506},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5836604237556458},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5715473294258118},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.5476239919662476},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.517296552658081},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.5077072381973267},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.452475905418396},{"id":"https://openalex.org/keywords/originality","display_name":"Originality","score":0.43946167826652527},{"id":"https://openalex.org/keywords/usability","display_name":"Usability","score":0.4214453101158142},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2556077837944031},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.1396876871585846},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.11308801174163818}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7916964292526245},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7519649267196655},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.731417715549469},{"id":"https://openalex.org/C87117476","wikidata":"https://www.wikidata.org/wiki/Q362383","display_name":"Cardinality (data modeling)","level":2,"score":0.6165902614593506},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5836604237556458},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5715473294258118},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.5476239919662476},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.517296552658081},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.5077072381973267},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.452475905418396},{"id":"https://openalex.org/C2776950860","wikidata":"https://www.wikidata.org/wiki/Q2914681","display_name":"Originality","level":3,"score":0.43946167826652527},{"id":"https://openalex.org/C170130773","wikidata":"https://www.wikidata.org/wiki/Q216378","display_name":"Usability","level":2,"score":0.4214453101158142},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2556077837944031},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.1396876871585846},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.11308801174163818},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C11012388","wikidata":"https://www.wikidata.org/wiki/Q170658","display_name":"Creativity","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1108/dta-04-2020-0093","is_oa":false,"landing_page_url":"https://doi.org/10.1108/dta-04-2020-0093","pdf_url":null,"source":{"id":"https://openalex.org/S4210171756","display_name":"Data Technologies and Applications","issn_l":"2514-9288","issn":["2514-9288","2514-9318"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Technologies and Applications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.6899999976158142,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W214353307","https://openalex.org/W369254515","https://openalex.org/W1493312012","https://openalex.org/W1553313034","https://openalex.org/W1678356000","https://openalex.org/W1817561967","https://openalex.org/W1976186848","https://openalex.org/W1987872768","https://openalex.org/W1995252707","https://openalex.org/W2025827699","https://openalex.org/W2046649434","https://openalex.org/W2047029964","https://openalex.org/W2076269792","https://openalex.org/W2082548634","https://openalex.org/W2084341220","https://openalex.org/W2088794999","https://openalex.org/W2099937944","https://openalex.org/W2105340608","https://openalex.org/W2112841646","https://openalex.org/W2119821739","https://openalex.org/W2147409005","https://openalex.org/W2148086182","https://openalex.org/W2149706766","https://openalex.org/W2155841152","https://openalex.org/W2159259011","https://openalex.org/W2170505850","https://openalex.org/W2188988097","https://openalex.org/W2203422614","https://openalex.org/W2395445704","https://openalex.org/W2594108779","https://openalex.org/W2765467597","https://openalex.org/W2794534827","https://openalex.org/W2805391180","https://openalex.org/W2909298984","https://openalex.org/W2911964244","https://openalex.org/W2954936648","https://openalex.org/W2954996726","https://openalex.org/W2964641067","https://openalex.org/W3023026039","https://openalex.org/W4232515367","https://openalex.org/W4236137412","https://openalex.org/W4239510810","https://openalex.org/W4239855301"],"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/W4296079469"],"abstract_inverted_index":{"Purpose":[0],"Analysis":[1],"of":[2,18,25,40,44,114,160,172],"the":[3,15,22,38,57,72,77,95,145],"publisher's":[4],"behavior":[5,79],"plays":[6],"a":[7,34],"vital":[8],"role":[9],"in":[10,14,36,47,75,112],"identifying":[11],"fraudulent":[12,88,99],"publishers":[13,100],"pay-per-click":[16],"model":[17],"online":[19],"advertising.":[20],"However,":[21],"vast":[23],"amount":[24],"raw":[26,81,134],"user":[27,82,135],"click":[28,83,136],"data":[29,84],"with":[30,50,178],"missing":[31],"values":[32,53],"pose":[33],"challenge":[35],"analyzing":[37],"conduct":[39],"publishers.":[41,89],"The":[42,91,126,158,170],"presence":[43],"high":[45],"cardinality":[46],"categorical":[48],"attributes":[49],"multiple":[51],"possible":[52],"has":[54],"further":[55],"aggrieved":[56],"issue.":[58],"Design/methodology/approach":[59],"In":[60],"this":[61],"paper,":[62],"gradient":[63],"tree":[64],"boosting":[65],"(GTB)":[66],"learning":[67,76,110,174],"is":[68],"used":[69],"to":[70,108,143],"address":[71],"challenges":[73],"encountered":[74],"publishers'":[78],"from":[80],"and":[85,101,122,138,151,168,183],"effectively":[86,97],"classifying":[87],"Findings":[90],"results":[92],"demonstrate":[93],"that":[94],"GTB":[96,161,173],"classified":[98],"exhibited":[102],"significantly":[103],"improved":[104],"performance":[105,159,171],"as":[106],"compared":[107,177],"other":[109,140,180],"methods":[111],"terms":[113],"average":[115,165],"precision":[116],"(60.5":[117],"%),":[118],"recall":[119,167],"(57.8":[120],"%)":[121],"f-measure":[123],"(59.1%).":[124],"Originality/value":[125],"experiments":[127],"were":[128,153],"conducted":[129],"using":[130,155,164],"publicly":[131],"available":[132],"multiclass":[133],"dataset":[137],"eight":[139],"imbalanced":[141],"datasets":[142],"test":[144],"GTB's":[146],"generalizing":[147],"behavior,":[148],"while":[149],"training":[150],"testing":[152],"done":[154],"10-fold":[156],"cross-validation.":[157],"was":[162,175],"evaluated":[163],"precision,":[166],"f-measure.":[169],"also":[176],"eleven":[179],"state-of-the-art":[181],"individual":[182],"ensemble":[184],"classification":[185],"models.":[186]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}