{"id":"https://openalex.org/W2951930001","doi":"https://doi.org/10.4018/ijghpc.2019070102","title":"Empirical Evaluation of Map Reduce Based Hybrid Approach for Problem of Imbalanced Classification in Big Data","display_name":"Empirical Evaluation of Map Reduce Based Hybrid Approach for Problem of Imbalanced Classification in Big Data","publication_year":2019,"publication_date":"2019-06-18","ids":{"openalex":"https://openalex.org/W2951930001","doi":"https://doi.org/10.4018/ijghpc.2019070102","mag":"2951930001"},"language":"en","primary_location":{"id":"doi:10.4018/ijghpc.2019070102","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijghpc.2019070102","pdf_url":null,"source":{"id":"https://openalex.org/S43307632","display_name":"International Journal of Grid and High Performance Computing","issn_l":"1938-0259","issn":["1938-0259","1938-0267"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Grid and High Performance Computing","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/A5066668860","display_name":"Khyati Ahlawat","orcid":null},"institutions":[{"id":"https://openalex.org/I4210143260","display_name":"Indira Gandhi Delhi Technical University for Women","ror":"https://ror.org/057c5p638","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210143260"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Khyati Ahlawat","raw_affiliation_strings":["IGDTUW, Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IGDTUW, Delhi, India","institution_ids":["https://openalex.org/I4210143260"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064896366","display_name":"Anuradha Chug","orcid":"https://orcid.org/0000-0002-3139-4490"},"institutions":[{"id":"https://openalex.org/I105454292","display_name":"Guru Gobind Singh Indraprastha University","ror":"https://ror.org/034q1za58","country_code":"IN","type":"education","lineage":["https://openalex.org/I105454292"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anuradha Chug","raw_affiliation_strings":["GGSIPU, Delhi, India"],"raw_orcid":"https://orcid.org/0000-0002-3139-4490","affiliations":[{"raw_affiliation_string":"GGSIPU, Delhi, India","institution_ids":["https://openalex.org/I105454292"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008002283","display_name":"Amit Prakash Singh","orcid":"https://orcid.org/0000-0002-8675-6903"},"institutions":[{"id":"https://openalex.org/I105454292","display_name":"Guru Gobind Singh Indraprastha University","ror":"https://ror.org/034q1za58","country_code":"IN","type":"education","lineage":["https://openalex.org/I105454292"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Amit Prakash Singh","raw_affiliation_strings":["GGSIPU, Delhi, India"],"raw_orcid":"https://orcid.org/0000-0002-8675-6903","affiliations":[{"raw_affiliation_string":"GGSIPU, Delhi, India","institution_ids":["https://openalex.org/I105454292"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5066668860"],"corresponding_institution_ids":["https://openalex.org/I4210143260"],"apc_list":null,"apc_paid":null,"fwci":0.7225,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.78392938,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"11","issue":"3","first_page":"23","last_page":"45"},"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/T13429","display_name":"Electricity Theft Detection Techniques","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9822999835014343,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7682207822799683},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7406705617904663},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7070069909095764},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6934003829956055},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6692609786987305},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6292930841445923},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5975241661071777},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5162737369537354},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36015623807907104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7682207822799683},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7406705617904663},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7070069909095764},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6934003829956055},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6692609786987305},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6292930841445923},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5975241661071777},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5162737369537354},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36015623807907104}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.4018/ijghpc.2019070102","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijghpc.2019070102","pdf_url":null,"source":{"id":"https://openalex.org/S43307632","display_name":"International Journal of Grid and High Performance Computing","issn_l":"1938-0259","issn":["1938-0259","1938-0267"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Grid and High Performance Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W379212275","https://openalex.org/W580366031","https://openalex.org/W619160221","https://openalex.org/W981727710","https://openalex.org/W1137625847","https://openalex.org/W1551909886","https://openalex.org/W1918839972","https://openalex.org/W1965558308","https://openalex.org/W1966547431","https://openalex.org/W1984606274","https://openalex.org/W1984867936","https://openalex.org/W1987860477","https://openalex.org/W1999318832","https://openalex.org/W2010089450","https://openalex.org/W2033174805","https://openalex.org/W2040263621","https://openalex.org/W2041328702","https://openalex.org/W2072750586","https://openalex.org/W2086207029","https://openalex.org/W2087295784","https://openalex.org/W2099454382","https://openalex.org/W2126623642","https://openalex.org/W2126734246","https://openalex.org/W2135074661","https://openalex.org/W2141921295","https://openalex.org/W2145611520","https://openalex.org/W2157954477","https://openalex.org/W2164330572","https://openalex.org/W2171647935","https://openalex.org/W2196145940","https://openalex.org/W2230801863","https://openalex.org/W2298182397","https://openalex.org/W2325552192","https://openalex.org/W2338318698","https://openalex.org/W2339974927","https://openalex.org/W2342457036","https://openalex.org/W2432436793","https://openalex.org/W2536062280","https://openalex.org/W2557427388","https://openalex.org/W2579247884","https://openalex.org/W2619580871","https://openalex.org/W2658182684","https://openalex.org/W2731426225","https://openalex.org/W4242320705","https://openalex.org/W4251199158"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4247566972","https://openalex.org/W2960264696","https://openalex.org/W3090563135","https://openalex.org/W2497432351","https://openalex.org/W4206777497","https://openalex.org/W4233347783","https://openalex.org/W2910064364","https://openalex.org/W4255224757","https://openalex.org/W2499527417"],"abstract_inverted_index":{"Imbalanced":[0],"datasets":[1,72,124],"are":[2],"the":[3,59,104,144,148],"ones":[4],"with":[5,21,49,133,138],"uneven":[6],"distribution":[7],"of":[8,34,87,94,106,147],"classes":[9],"that":[10,58],"deteriorates":[11],"classifier's":[12],"performance.":[13],"In":[14],"this":[15,88],"paper,":[16],"SVM":[17,69,140],"classifier":[18,68,141],"is":[19,30,37,90],"combined":[20],"K-Means":[22],"clustering":[23],"approach":[24],"and":[25,43,47,73,99,126,142],"a":[26,115],"hybrid":[27,61],"approach,":[28],"Hy_SVM_KM":[29],"introduced.":[31],"The":[32,54,85],"performance":[33,146],"proposed":[35,60],"method":[36],"also":[38],"empirically":[39],"evaluated":[40],"using":[41],"Accuracy":[42],"FN":[44],"Rate":[45],"measure":[46],"compared":[48],"existing":[50],"methods":[51],"like":[52],"SMOTE.":[53],"results":[55],"have":[56,74],"shown":[57],"technique":[62,80],"has":[63],"outperformed":[64],"traditional":[65],"machine":[66,96],"learning":[67,97],"in":[70],"mostly":[71],"performed":[75],"better":[76],"than":[77],"known":[78],"pre-processing":[79],"SMOTE":[81],"for":[82,118],"all":[83],"datasets.":[84],"goal":[86],"article":[89,112],"to":[91,102,131],"extend":[92],"capabilities":[93],"popular":[95],"algorithms":[98],"adapt":[100],"it":[101],"meet":[103],"challenges":[105],"imbalanced":[107,122,134],"big":[108,123,136],"data":[109],"classification.":[110],"This":[111],"can":[113],"provide":[114],"baseline":[116],"study":[117],"future":[119],"research":[120],"on":[121],"classification":[125],"provides":[127],"an":[128],"efficient":[129],"mechanism":[130],"deal":[132],"nature":[135],"dataset":[137],"modified":[139],"improves":[143],"overall":[145],"model.":[149]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}