{"id":"https://openalex.org/W2293522866","doi":"https://doi.org/10.5220/0005593000390049","title":"Gaussian Process for Regression in Business Intelligence: A Fraud Detection Application","display_name":"Gaussian Process for Regression in Business Intelligence: A Fraud Detection Application","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W2293522866","doi":"https://doi.org/10.5220/0005593000390049","mag":"2293522866"},"language":"en","primary_location":{"id":"doi:10.5220/0005593000390049","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0005593000390049","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.5220/0005593000390049","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108619824","display_name":"Bruno H. A. Pilon","orcid":null},"institutions":[{"id":"https://openalex.org/I150729083","display_name":"Universidade de Bras\u00edlia","ror":"https://ror.org/02xfp8v59","country_code":"BR","type":"education","lineage":["https://openalex.org/I150729083"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Bruno H. A. Pilon","raw_affiliation_strings":["University of Brasilia (UnB), Brazil"],"affiliations":[{"raw_affiliation_string":"University of Brasilia (UnB), Brazil","institution_ids":["https://openalex.org/I150729083"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036960709","display_name":"Juan Jos\u00e9 Murillo-Fuentes","orcid":"https://orcid.org/0000-0002-9041-0147"},"institutions":[{"id":"https://openalex.org/I79238269","display_name":"Universidad de Sevilla","ror":"https://ror.org/03yxnpp24","country_code":"ES","type":"education","lineage":["https://openalex.org/I79238269"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Juan J. Murillo-Fuentes","raw_affiliation_strings":["University of Sevilla, Spain"],"affiliations":[{"raw_affiliation_string":"University of Sevilla, Spain","institution_ids":["https://openalex.org/I79238269"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069416979","display_name":"Jo\u00e3o Paulo C. L. da Costa","orcid":"https://orcid.org/0000-0002-8616-4924"},"institutions":[{"id":"https://openalex.org/I150729083","display_name":"Universidade de Bras\u00edlia","ror":"https://ror.org/02xfp8v59","country_code":"BR","type":"education","lineage":["https://openalex.org/I150729083"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Jo\u00e3o Paulo C. L. da Costa","raw_affiliation_strings":["University of Brasilia (UnB), Brazil"],"affiliations":[{"raw_affiliation_string":"University of Brasilia (UnB), Brazil","institution_ids":["https://openalex.org/I150729083"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056962045","display_name":"Rafael T. de Sousa","orcid":"https://orcid.org/0000-0003-1101-3029"},"institutions":[{"id":"https://openalex.org/I150729083","display_name":"Universidade de Bras\u00edlia","ror":"https://ror.org/02xfp8v59","country_code":"BR","type":"education","lineage":["https://openalex.org/I150729083"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Rafael T. de Sousa J\u00fanior","raw_affiliation_strings":["University of Brasilia (UnB), Brazil"],"affiliations":[{"raw_affiliation_string":"University of Brasilia (UnB), Brazil","institution_ids":["https://openalex.org/I150729083"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073599975","display_name":"Antonio M. Rubio Serrano","orcid":null},"institutions":[{"id":"https://openalex.org/I150729083","display_name":"Universidade de Bras\u00edlia","ror":"https://ror.org/02xfp8v59","country_code":"BR","type":"education","lineage":["https://openalex.org/I150729083"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Antonio M. R. Serrano","raw_affiliation_strings":["University of Brasilia (UnB), Brazil"],"affiliations":[{"raw_affiliation_string":"University of Brasilia (UnB), Brazil","institution_ids":["https://openalex.org/I150729083"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5108619824"],"corresponding_institution_ids":["https://openalex.org/I150729083"],"apc_list":null,"apc_paid":null,"fwci":1.3353,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.87120415,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"39","last_page":"49"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9843999743461609,"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":0.9843999743461609,"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/T11891","display_name":"Big Data and Business Intelligence","score":0.982699990272522,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.982200026512146,"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":0.7887251377105713},{"id":"https://openalex.org/keywords/ground-penetrating-radar","display_name":"Ground-penetrating radar","score":0.7076330184936523},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.6317682266235352},{"id":"https://openalex.org/keywords/business-intelligence","display_name":"Business intelligence","score":0.5715615749359131},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.559415340423584},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5121057033538818},{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.5110088586807251},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4917440712451935},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4852001965045929},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4784686267375946},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.43910956382751465},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.31278786063194275},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.11569172143936157}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7887251377105713},{"id":"https://openalex.org/C71813955","wikidata":"https://www.wikidata.org/wiki/Q503560","display_name":"Ground-penetrating radar","level":3,"score":0.7076330184936523},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6317682266235352},{"id":"https://openalex.org/C2767350","wikidata":"https://www.wikidata.org/wiki/Q6662173","display_name":"Business intelligence","level":2,"score":0.5715615749359131},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.559415340423584},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5121057033538818},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.5110088586807251},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4917440712451935},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4852001965045929},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4784686267375946},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.43910956382751465},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.31278786063194275},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.11569172143936157},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5220/0005593000390049","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0005593000390049","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.712.1855","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.712.1855","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.redes.unb.br/lasp/files/papers/KMIS_2015_Pilon.pdf","raw_type":"text"}],"best_oa_location":{"id":"doi:10.5220/0005593000390049","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0005593000390049","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.800000011920929,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W196162695","https://openalex.org/W1584826989","https://openalex.org/W1602011302","https://openalex.org/W1963956309","https://openalex.org/W2040970380","https://openalex.org/W2130475491","https://openalex.org/W2131241448","https://openalex.org/W2167374789","https://openalex.org/W3026026050","https://openalex.org/W3140968660"],"related_works":["https://openalex.org/W4315471419","https://openalex.org/W566010457","https://openalex.org/W2600092203","https://openalex.org/W4300066510","https://openalex.org/W2056958800","https://openalex.org/W2803685231","https://openalex.org/W4293503520","https://openalex.org/W3134152097","https://openalex.org/W4311388919","https://openalex.org/W2966696655"],"abstract_inverted_index":{"Abstract:":[0],"Business":[1],"Intelligence":[2],"(BI)":[3],"systems":[4,104],"are":[5],"designed":[6],"to":[7,10,53,65,82,95,107,162,194],"provide":[8],"information":[9],"support":[11],"the":[12,28,32,36,48,58,61,88,109,133,138,151,179,186,198,212],"decision":[13,49],"making":[14,50],"process":[15,51],"in":[16,102,105,113,137],"companies":[17],"and":[18,30,52,60,76,165],"governmental":[19],"institutions.":[20],"In":[21,90,192],"this":[22,91,114,202],"scenario,":[23],"future":[24],"events":[25],"depend":[26],"on":[27,31,57],"decisions":[29],"previous":[33],"events.":[34],"Therefore,":[35],"mathematical":[37],"analysis":[38],"of":[39,63,87,122,135,150,189,201],"past":[40],"data":[41,64,74,176],"can":[42,79,155],"be":[43,66,80,156],"an":[44],"important":[45,120],"tool":[46],"for":[47,99,210],"detect":[54],"anomalies.":[55],"Depending":[56],"amount":[59],"type":[62],"analyzed,":[67],"techniques":[68],"from":[69],"statistics,":[70],"Machine":[71],"Learning":[72],"(ML),":[73],"mining":[75],"signal":[77],"processing":[78],"used":[81,157],"automate":[83],"all":[84],"or":[85],"part":[86],"system.":[89],"paper,":[92],"we":[93,205],"propose":[94,206],"incorporate":[96],"Gaussian":[97],"Process":[98],"Regression":[100],"(GPR)":[101],"BI":[103,123,175],"order":[106,193],"predict":[108],"data.":[110,167],"As":[111],"presented":[112],"work,":[115],"fraud":[116],"detection":[117],"is":[118,130],"one":[119],"application":[121,129],"systems.":[124],"We":[125,168],"show":[126],"that":[127,142],"such":[128,232],"possible":[131],"with":[132,172,223],"use":[134],"GPR":[136,143,224],"predictive":[139,230],"stage,":[140],"considering":[141],"natively":[144],"returns":[145],"a":[146,159,207,217],"full":[147],"statistical":[148],"description":[149],"estimated":[152],"variable,":[153],"which":[154],"as":[158,233],"trigger":[160],"measure":[161],"classify":[163],"trusted":[164],"untrusted":[166],"validate":[169],"our":[170],"proposal":[171],"real":[173],"world":[174],"provided":[177],"by":[178],"Brazilian":[180],"Federal":[181],"Patrimony":[182],"Department":[183],"(SPU),":[184],"regarding":[185],"monthly":[187],"collection":[188],"federal":[190],"taxes.":[191],"take":[195],"into":[196,216],"account":[197],"multidimensional":[199],"structure":[200],"specific":[203],"data,":[204],"pre-processing":[208],"stage":[209],"reshaping":[211],"original":[213],"time":[214],"series":[215],"bidimensional":[218],"structure.":[219],"The":[220],"resulting":[221],"algorithm,":[222],"at":[225],"its":[226],"core,":[227],"outperforms":[228],"classical":[229],"schemes":[231],"Artificial":[234],"Neural":[235],"Network":[236],"(ANN).":[237],"1":[238]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}