{"id":"https://openalex.org/W2920835217","doi":"https://doi.org/10.26599/bdma.2018.9020032","title":"Feature selection with graph mining technology","display_name":"Feature selection with graph mining technology","publication_year":2019,"publication_date":"2019-03-14","ids":{"openalex":"https://openalex.org/W2920835217","doi":"https://doi.org/10.26599/bdma.2018.9020032","mag":"2920835217"},"language":"en","primary_location":{"id":"doi:10.26599/bdma.2018.9020032","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2018.9020032","pdf_url":"https://ieeexplore.ieee.org/ielx7/8254253/8665382/08665725.pdf","source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ieeexplore.ieee.org/ielx7/8254253/8665382/08665725.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062322117","display_name":"Thosini Bamunu Mudiyanselage","orcid":"https://orcid.org/0000-0002-0964-7826"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Thosini Bamunu Mudiyanselage","raw_affiliation_strings":["Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100612105","display_name":"Yanqing Zhang","orcid":"https://orcid.org/0000-0003-2349-1925"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanqing Zhang","raw_affiliation_strings":["Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA","institution_ids":["https://openalex.org/I181565077"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5062322117"],"corresponding_institution_ids":["https://openalex.org/I181565077"],"apc_list":null,"apc_paid":null,"fwci":2.4678,"has_fulltext":true,"cited_by_count":10,"citation_normalized_percentile":{"value":0.91090851,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"2","issue":"2","first_page":"73","last_page":"82"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9934999942779541,"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"}},"topics":[{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9934999942779541,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9690999984741211,"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/T10057","display_name":"Face and Expression Recognition","score":0.9369999766349792,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/feature-selection","display_name":"Feature selection","score":0.7840509414672852},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7295860052108765},{"id":"https://openalex.org/keywords/minimum-redundancy-feature-selection","display_name":"Minimum redundancy feature selection","score":0.5917214155197144},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5658859014511108},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.554655134677887},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5293532013893127},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5167455077171326},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5107266902923584},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.49746015667915344},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4946615397930145},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4907878339290619},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.47632771730422974},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.4641706943511963},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.460429847240448},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35282236337661743},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.23234212398529053}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7840509414672852},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7295860052108765},{"id":"https://openalex.org/C16811321","wikidata":"https://www.wikidata.org/wiki/Q17138905","display_name":"Minimum redundancy feature selection","level":3,"score":0.5917214155197144},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5658859014511108},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.554655134677887},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5293532013893127},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5167455077171326},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5107266902923584},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.49746015667915344},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4946615397930145},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4907878339290619},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.47632771730422974},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.4641706943511963},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.460429847240448},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35282236337661743},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.23234212398529053},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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":2,"locations":[{"id":"doi:10.26599/bdma.2018.9020032","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2018.9020032","pdf_url":"https://ieeexplore.ieee.org/ielx7/8254253/8665382/08665725.pdf","source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:51216d57c7ae4f39aa729063d30bc32f","is_oa":true,"landing_page_url":"https://doaj.org/article/51216d57c7ae4f39aa729063d30bc32f","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Big Data Mining and Analytics, Vol 2, Iss 2, Pp 73-82 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.26599/bdma.2018.9020032","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2018.9020032","pdf_url":"https://ieeexplore.ieee.org/ielx7/8254253/8665382/08665725.pdf","source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2920835217.pdf","grobid_xml":"https://content.openalex.org/works/W2920835217.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W1593753825","https://openalex.org/W1685464609","https://openalex.org/W1964042173","https://openalex.org/W2027902860","https://openalex.org/W2035043933","https://openalex.org/W2088937912","https://openalex.org/W2092845575","https://openalex.org/W2095378398","https://openalex.org/W2128873747","https://openalex.org/W2132379769","https://openalex.org/W2149620660","https://openalex.org/W2154636369","https://openalex.org/W2260771218","https://openalex.org/W2335437633","https://openalex.org/W2466939964","https://openalex.org/W2575552627","https://openalex.org/W2581572661","https://openalex.org/W2606926586","https://openalex.org/W2734601688","https://openalex.org/W2765158981","https://openalex.org/W2769509511","https://openalex.org/W2809701111","https://openalex.org/W2883604340","https://openalex.org/W2896808365","https://openalex.org/W6600234944","https://openalex.org/W6835552651"],"related_works":["https://openalex.org/W1995622179","https://openalex.org/W1484111231","https://openalex.org/W1552543208","https://openalex.org/W2074396517","https://openalex.org/W2166963679","https://openalex.org/W2187269125","https://openalex.org/W1641615907","https://openalex.org/W3089231081","https://openalex.org/W2093956241","https://openalex.org/W2354420595"],"abstract_inverted_index":{"Many":[0],"real":[1],"world":[2],"applications":[3],"have":[4],"problems":[5],"with":[6,136],"high":[7],"dimensionality,":[8],"which":[9,54],"existing":[10,66],"algorithms":[11,171],"cannot":[12],"overcome.":[13],"A":[14],"critical":[15],"data":[16,34,100,141],"preprocessing":[17],"problem":[18,50,167],"is":[19,150,163,192],"feature":[20,63,96,134],"selection,":[21],"whereby":[22],"its":[23],"non-scalability":[24],"negatively":[25],"influences":[26],"both":[27],"the":[28,46,56,60,84,92,95,98,105,126,132,139,157],"efficiency":[29],"and":[30,130],"performance":[31],"of":[32,48,59,79,94,159,212],"big":[33],"applications.":[35],"In":[36,175],"this":[37,116,121,176,201],"research,":[38],"we":[39],"developed":[40,109],"a":[41,49,72,110,164,185],"new":[42,111],"algorithm":[43,86,187,216],"to":[44,102,114,124,138,173,188,194],"reduce":[45],"dimensionality":[47],"using":[51],"graph-based":[52],"analysis,":[53],"retains":[55],"physical":[57],"meaning":[58],"original":[61,106,140],"high-dimensional":[62],"space.":[64],"Most":[65],"feature-selection":[67,85,112,170],"methods":[68],"are":[69,77],"based":[70],"on":[71],"strong":[73],"assumption":[74],"that":[75,151,168,183,196],"features":[76,129],"independent":[78,211],"each":[80],"other.":[81],"However,":[82],"if":[83],"does":[87],"not":[88],"take":[89],"into":[90],"consideration":[91],"interdependencies":[93],"space,":[97],"selected":[99],"fail":[101,172],"correctly":[103],"represent":[104],"data.":[107],"We":[108],"method":[113,149,205],"address":[115],"challenge.":[117],"Our":[118],"aim":[119],"in":[120,146,156],"research":[122],"was":[123],"examine":[125],"dependencies":[127],"between":[128],"select":[131,189],"optimal":[133],"set":[135],"respect":[137],"structure.":[142],"Another":[143],"important":[144,193],"factor":[145],"our":[147,197],"proposed":[148,202],"it":[152],"can":[153],"perform":[154],"even":[155],"absence":[158],"class":[160],"labels.":[161],"This":[162],"more":[165],"difficult":[166],"many":[169],"address.":[174],"case,":[177],"they":[178],"only":[179],"use":[180],"wrapper":[181],"techniques":[182],"require":[184],"learning":[186,215],"features.":[190],"It":[191],"note":[195],"experimental":[198],"results":[199],"indicates,":[200],"simple":[203],"ranking":[204],"performs":[206],"better":[207],"than":[208],"other":[209],"methods,":[210],"any":[213],"particular":[214],"used.":[217]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":5}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}