{"id":"https://openalex.org/W2965043789","doi":"https://doi.org/10.1117/12.2540457","title":"Exploring data sampling techniques for imbalanced classification problems","display_name":"Exploring data sampling techniques for imbalanced classification problems","publication_year":2019,"publication_date":"2019-07-31","ids":{"openalex":"https://openalex.org/W2965043789","doi":"https://doi.org/10.1117/12.2540457","mag":"2965043789"},"language":"en","primary_location":{"id":"doi:10.1117/12.2540457","doi":"https://doi.org/10.1117/12.2540457","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2540457","pdf_url":null,"source":{"id":"https://openalex.org/S7407088962","display_name":"International Workshop on Pattern Recognition (IWPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Fourth International Workshop on Pattern Recognition","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5068070564","display_name":"Yu Sui","orcid":"https://orcid.org/0009-0003-5413-4805","observed_orcids":["https://orcid.org/0009-0003-5413-4805"]},"institutions":[{"id":"https://openalex.org/I4405272762","display_name":"Guangdong Power Grid Company (China)","ror":"https://ror.org/04p8atc80","country_code":"CN","type":"company","lineage":["https://openalex.org/I4405272762"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Sui","raw_affiliation_strings":["Guangdong Power Grid Corp. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangdong Power Grid Corp. (China)","institution_ids":["https://openalex.org/I4405272762"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100400244","display_name":"Xiaohui Zhang","orcid":"https://orcid.org/0000-0002-9949-5415","observed_orcids":["https://orcid.org/0000-0002-9949-5415","https://orcid.org/0000-0002-9075-0336"]},"institutions":[{"id":"https://openalex.org/I4405272762","display_name":"Guangdong Power Grid Company (China)","ror":"https://ror.org/04p8atc80","country_code":"CN","type":"company","lineage":["https://openalex.org/I4405272762"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaohui Zhang","raw_affiliation_strings":["Guangdong Power Grid Corp. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangdong Power Grid Corp. (China)","institution_ids":["https://openalex.org/I4405272762"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013178743","display_name":"Jiajia Huan","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405272762","display_name":"Guangdong Power Grid Company (China)","ror":"https://ror.org/04p8atc80","country_code":"CN","type":"company","lineage":["https://openalex.org/I4405272762"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiajia Huan","raw_affiliation_strings":["Guangdong Power Grid Corp. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangdong Power Grid Corp. (China)","institution_ids":["https://openalex.org/I4405272762"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102350161","display_name":"Haifeng Hong","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405272762","display_name":"Guangdong Power Grid Company (China)","ror":"https://ror.org/04p8atc80","country_code":"CN","type":"company","lineage":["https://openalex.org/I4405272762"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haifeng Hong","raw_affiliation_strings":["Guangdong Power Grid Corp. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangdong Power Grid Corp. (China)","institution_ids":["https://openalex.org/I4405272762"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4405272762"],"apc_list":null,"apc_paid":null,"fwci":0.1626,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.39360123,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"21","last_page":"21"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":1.0,"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":1.0,"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/T12535","display_name":"Machine Learning and Data Classification","score":9.999999747378752e-05,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.0,"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/class-imbalance","display_name":"class imbalance","score":0.875},{"id":"https://openalex.org/keywords/imbalanced-data-classification","display_name":"imbalanced classification","score":0.8659999966621399},{"id":"https://openalex.org/keywords/oversampling","display_name":"oversampling","score":0.6600000262260437},{"id":"https://openalex.org/keywords/undersampling","display_name":"undersampling","score":0.9700000286102295},{"id":"https://openalex.org/keywords/cluster-sampling","display_name":"cluster sampling","score":0.8980000019073486},{"id":"https://openalex.org/keywords/ensemble-sampling","display_name":"ensemble sampling","score":0.8180000185966492},{"id":"https://openalex.org/keywords/random-sampling","display_name":"random sampling","score":0.7829999923706055},{"id":"https://openalex.org/keywords/c4-5-decision-tree","display_name":"C4.5 decision tree","score":0.9900000095367432},{"id":"https://openalex.org/keywords/classifier-evaluation","display_name":"classifier evaluation","score":0.5350000262260437},{"id":"https://openalex.org/keywords/precision","display_name":"precision","score":0.7360000014305115},{"id":"https://openalex.org/keywords/g-measures","display_name":"g-measures","score":0.9010000228881836},{"id":"https://openalex.org/keywords/cohen-s-kappa","display_name":"Cohen's kappa","score":0.8199999928474426}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7500066757202148},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6194849610328674},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.606354832649231},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5965102910995483},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5951251983642578},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5334295034408569},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5079565644264221},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.5076802968978882},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.46548715233802795},{"id":"https://openalex.org/C5481197","wikidata":"https://www.wikidata.org/wiki/Q16766476","display_name":"Decision tree learning","level":3,"score":0.45177099108695984},{"id":"https://openalex.org/C167723999","wikidata":"https://www.wikidata.org/wiki/Q3773214","display_name":"Sampling distribution","level":2,"score":0.4304952323436737},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.21087893843650818},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20510396361351013},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2540457","doi":"https://doi.org/10.1117/12.2540457","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2540457","pdf_url":null,"source":{"id":"https://openalex.org/S7407088962","display_name":"International Workshop on Pattern Recognition (IWPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Fourth International Workshop on Pattern Recognition","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W48170608","https://openalex.org/W58207909","https://openalex.org/W85350352","https://openalex.org/W179756838","https://openalex.org/W203932818","https://openalex.org/W1517113043","https://openalex.org/W2025886727","https://openalex.org/W2058732827","https://openalex.org/W2103614420","https://openalex.org/W2106479238","https://openalex.org/W2118978333","https://openalex.org/W2123170466","https://openalex.org/W2125055259","https://openalex.org/W2148143831","https://openalex.org/W2325374849","https://openalex.org/W2504782928","https://openalex.org/W2740195871","https://openalex.org/W3005805959","https://openalex.org/W3120740533","https://openalex.org/W4230276398","https://openalex.org/W4251358816","https://openalex.org/W6608286391"],"related_works":["https://openalex.org/W2982002047","https://openalex.org/W4387615712","https://openalex.org/W4293328933","https://openalex.org/W2058165281","https://openalex.org/W4367153229","https://openalex.org/W4234746031","https://openalex.org/W4378422442","https://openalex.org/W2547783230","https://openalex.org/W2372141336","https://openalex.org/W6905146629"],"abstract_inverted_index":{"The":[0,97],"class":[1,66],"imbalance":[2],"problem":[3],"is":[4,45,51,61,99,145,155],"one":[5],"of":[6,24,33,57,70,73,89,107,110,126],"the":[7,21,29,71,108,112,140,149,153],"key":[8],"challenges":[9],"in":[10,20,40,64,68,105,115],"machine":[11],"learning":[12],"and":[13,49,75,136,152,161],"data":[14,17,34,58,74,91,117],"mining.":[15],"Imbalanced":[16],"can":[18],"result":[19],"sub-optimal":[22],"performance":[23,114,154],"classification":[25,113],"models.":[26],"To":[27],"address":[28],"problem,":[30],"a":[31,87],"variety":[32],"sampling":[35,59,103,124,135],"methods":[36,125],"have":[37],"been":[38],"proposed":[39],"previous":[41],"studies.":[42],"However,":[43],"there":[44],"no":[46],"universal":[47],"solution":[48],"it":[50],"worth":[52],"to":[53,100,147],"explore":[54],"which":[55],"kind":[56],"technique":[60],"more":[62],"effective":[63],"balancing":[65],"distribution":[67],"terms":[69,106],"type":[72],"classifier.":[76],"In":[77,119],"this":[78],"work,":[79],"we":[80,121],"present":[81],"an":[82],"experimental":[83],"study":[84,122],"based":[85],"on":[86],"number":[88],"real-world":[90],"sets":[92],"obtained":[93],"from":[94],"different":[95,102,127],"disciplines.":[96],"goal":[98],"investigate":[101],"techniques":[104],"effectiveness":[109],"increasing":[111],"imbalanced":[116],"sets.":[118],"particular,":[120],"ten":[123],"types,":[128],"including":[129],"random":[130],"sampling,":[131,133],"clusterbased":[132],"ensemble":[134],"so":[137],"on.":[138],"Besides,":[139],"C4.5":[141],"decision":[142],"tree":[143],"algorithm":[144],"used":[146],"train":[148],"base":[150],"classifiers":[151],"measured":[156],"by":[157],"using":[158],"precision,":[159],"G-Measure":[160],"Cohen's":[162],"Kappa":[163],"statistic.":[164]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2019-08-13T00:00:00"}
