{"id":"https://openalex.org/W4231284442","doi":"https://doi.org/10.1109/icca49400.2020.9022843","title":"Credit Card Fraud Detection Using Online Boosting with Extremely Fast Decision Tree","display_name":"Credit Card Fraud Detection Using Online Boosting with Extremely Fast Decision Tree","publication_year":2020,"publication_date":"2020-02-01","ids":{"openalex":"https://openalex.org/W4231284442","doi":"https://doi.org/10.1109/icca49400.2020.9022843"},"language":"en","primary_location":{"id":"doi:10.1109/icca49400.2020.9022843","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icca49400.2020.9022843","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Conference on Computer Applications(ICCA)","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/A5080496899","display_name":"Aye Aye Khine","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Aye Aye Khine","raw_affiliation_strings":["Associate Professor, ICTRC, Yangon, Myanmr"],"affiliations":[{"raw_affiliation_string":"Associate Professor, ICTRC, Yangon, Myanmr","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049961893","display_name":"Hint Wint Khin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hint Wint Khin","raw_affiliation_strings":["Associate Professor, ICTRC, Yangon, Myanmr"],"affiliations":[{"raw_affiliation_string":"Associate Professor, ICTRC, Yangon, Myanmr","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5080496899"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.193,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.84188694,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9991999864578247,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9991999864578247,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9987999796867371,"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/T10270","display_name":"Blockchain Technology Applications and Security","score":0.9696000218391418,"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/computer-science","display_name":"Computer science","score":0.8181418776512146},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7073436975479126},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6922491788864136},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.6630148887634277},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.6608829498291016},{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept drift","score":0.6584933996200562},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6004595160484314},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.5837011933326721},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5239893794059753},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5224446654319763},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4637584984302521},{"id":"https://openalex.org/keywords/credit-card","display_name":"Credit card","score":0.4517206847667694},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4465571641921997},{"id":"https://openalex.org/keywords/streaming-data","display_name":"Streaming data","score":0.42574629187583923},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.13476788997650146}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8181418776512146},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7073436975479126},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6922491788864136},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6630148887634277},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.6608829498291016},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.6584933996200562},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6004595160484314},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.5837011933326721},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5239893794059753},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5224446654319763},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4637584984302521},{"id":"https://openalex.org/C2983355114","wikidata":"https://www.wikidata.org/wiki/Q161380","display_name":"Credit card","level":3,"score":0.4517206847667694},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4465571641921997},{"id":"https://openalex.org/C2777611316","wikidata":"https://www.wikidata.org/wiki/Q39045282","display_name":"Streaming data","level":2,"score":0.42574629187583923},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.13476788997650146},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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/C145097563","wikidata":"https://www.wikidata.org/wiki/Q1148747","display_name":"Payment","level":2,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"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/icca49400.2020.9022843","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icca49400.2020.9022843","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Conference on Computer Applications(ICCA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7200000286102295,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W13530706","https://openalex.org/W435157458","https://openalex.org/W1485123727","https://openalex.org/W1497927080","https://openalex.org/W1529840045","https://openalex.org/W1536228795","https://openalex.org/W1807914171","https://openalex.org/W1876956220","https://openalex.org/W2068714596","https://openalex.org/W2084433447","https://openalex.org/W2125573488","https://openalex.org/W2146159678","https://openalex.org/W2167089254","https://openalex.org/W2255183448","https://openalex.org/W2585528949","https://openalex.org/W2602516395","https://openalex.org/W2904738814","https://openalex.org/W2962774976","https://openalex.org/W2963375472","https://openalex.org/W4386542944","https://openalex.org/W6600552881","https://openalex.org/W6628844861","https://openalex.org/W6639055646"],"related_works":["https://openalex.org/W4281572076","https://openalex.org/W4307392573","https://openalex.org/W2802243998","https://openalex.org/W2736127210","https://openalex.org/W4389449520","https://openalex.org/W2329342202","https://openalex.org/W2060628068","https://openalex.org/W3208495060","https://openalex.org/W4246591448","https://openalex.org/W2235038291"],"abstract_inverted_index":{"Nowadays,":[0],"data":[1,26,38,40,91,94],"stream":[2,41,95],"mining":[3,92],"is":[4,70,120],"a":[5,139],"very":[6],"hot":[7],"and":[8,36,57,63,66,109],"high":[9,54],"attention":[10],"research":[11],"field":[12],"due":[13],"to":[14,51,80,98,135],"the":[15,29,84,123,147,160],"real-time":[16],"industrial":[17],"applications":[18],"from":[19],"different":[20],"sources":[21],"are":[22,76],"generating":[23],"amount":[24],"of":[25,74,86,146],"continuously":[27],"as":[28,129],"streaming":[30],"style.":[31],"To":[32],"process":[33],"these":[34],"growing":[35],"large":[37],"streams,":[39],"mining,":[42],"classification":[43],"algorithms":[44,49],"have":[45,50],"been":[46],"proposed.":[47],"These":[48],"deal":[52],"with":[53,104,159],"processing":[55],"time":[56,110],"memory":[58,108],"costs,":[59,111],"class":[60],"imbalance,":[61],"overfitting":[62],"concept":[64],"drift":[65],"so":[67],"on.":[68],"It":[69],"sure":[71],"that":[72],"ensembles":[73],"classifiers":[75,88],"being":[77],"effectively":[78],"used":[79],"make":[81],"improvement":[82],"in":[83,89,102,133],"accuracy":[85],"single":[87,140],"either":[90],"or":[93],"mining.":[96],"Thus,":[97],"get":[99],"higher":[100],"performance":[101],"prediction":[103],"largely":[105],"no":[106],"increasing":[107],"this":[112],"paper":[113],"proposes":[114],"an":[115],"Online":[116],"Boosting(OLBoost)":[117],"Approach,":[118],"which":[119],"firstly":[121],"use":[122],"Extremely":[124],"Fast":[125],"Decision":[126],"Tree":[127],"(EFDT)":[128],"base":[130],"(weak)":[131],"learner,":[132],"order":[134],"ensemble":[136],"them":[137],"into":[138],"online":[141],"strong":[142],"learner.":[143],"The":[144],"experiments":[145],"proposed":[148],"method":[149],"were":[150],"carried":[151],"out":[152],"for":[153],"credit":[154],"card":[155],"fraud":[156],"detection":[157],"domain":[158],"sample":[161],"benchmark":[162],"datasets.":[163]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}