{"id":"https://openalex.org/W4226151920","doi":"https://doi.org/10.1515/comp-2020-0228","title":"Rough set-based entropy measure with weighted density outlier detection method","display_name":"Rough set-based entropy measure with weighted density outlier detection method","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4226151920","doi":"https://doi.org/10.1515/comp-2020-0228"},"language":"en","primary_location":{"id":"doi:10.1515/comp-2020-0228","is_oa":true,"landing_page_url":"https://doi.org/10.1515/comp-2020-0228","pdf_url":"https://www.degruyter.com/document/doi/10.1515/comp-2020-0228/pdf","source":{"id":"https://openalex.org/S4210177004","display_name":"Open Computer Science","issn_l":"2299-1093","issn":["2299-1093"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310313990","host_organization_name":"De Gruyter","host_organization_lineage":["https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Open Computer Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.degruyter.com/document/doi/10.1515/comp-2020-0228/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5022749596","display_name":"T. Sangeetha","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Tamilarasu Sangeetha","raw_affiliation_strings":["School of Computer Science and Engineering, Vellore Institute of Technology , Vellore 632 001 , Tamil Nadu , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Vellore Institute of Technology , Vellore 632 001 , Tamil Nadu , India","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"last","author":{"id":null,"display_name":"Amalanathan Geetha Mary","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Amalanathan Geetha Mary","raw_affiliation_strings":["School of Computer Science and Engineering, Vellore Institute of Technology , Vellore 632 001 , Tamil Nadu , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Vellore Institute of Technology , Vellore 632 001 , Tamil Nadu , India","institution_ids":["https://openalex.org/I876193797"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I876193797"],"apc_list":{"value":1000,"currency":"EUR","value_usd":1078},"apc_paid":{"value":1000,"currency":"EUR","value_usd":1078},"fwci":0.4571,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.64712111,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":"12","issue":"1","first_page":"123","last_page":"133"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.963100016117096,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9045000076293945,"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/rough-set","display_name":"Rough set","score":0.839431643486023},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6805780529975891},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.6192941069602966},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6103253364562988},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5961872339248657},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5720497965812683},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5228305459022522},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.48080626130104065},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4133906662464142},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3713162839412689}],"concepts":[{"id":"https://openalex.org/C111012933","wikidata":"https://www.wikidata.org/wiki/Q3137210","display_name":"Rough set","level":2,"score":0.839431643486023},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6805780529975891},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.6192941069602966},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6103253364562988},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5961872339248657},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5720497965812683},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5228305459022522},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.48080626130104065},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4133906662464142},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3713162839412689},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1515/comp-2020-0228","is_oa":true,"landing_page_url":"https://doi.org/10.1515/comp-2020-0228","pdf_url":"https://www.degruyter.com/document/doi/10.1515/comp-2020-0228/pdf","source":{"id":"https://openalex.org/S4210177004","display_name":"Open Computer Science","issn_l":"2299-1093","issn":["2299-1093"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310313990","host_organization_name":"De Gruyter","host_organization_lineage":["https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Open Computer Science","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:b1b20f5e66904861ae6b0cc7db18a7ed","is_oa":true,"landing_page_url":"https://doaj.org/article/b1b20f5e66904861ae6b0cc7db18a7ed","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":"Open Computer Science, Vol 12, Iss 1, Pp 123-133 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1515/comp-2020-0228","is_oa":true,"landing_page_url":"https://doi.org/10.1515/comp-2020-0228","pdf_url":"https://www.degruyter.com/document/doi/10.1515/comp-2020-0228/pdf","source":{"id":"https://openalex.org/S4210177004","display_name":"Open Computer Science","issn_l":"2299-1093","issn":["2299-1093"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310313990","host_organization_name":"De Gruyter","host_organization_lineage":["https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Open Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4226151920.pdf","grobid_xml":"https://content.openalex.org/works/W4226151920.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W102936894","https://openalex.org/W792186058","https://openalex.org/W1021271351","https://openalex.org/W1510037165","https://openalex.org/W1554773601","https://openalex.org/W1976555061","https://openalex.org/W2016000166","https://openalex.org/W2016006067","https://openalex.org/W2025172647","https://openalex.org/W2040753112","https://openalex.org/W2077006143","https://openalex.org/W2078032741","https://openalex.org/W2092491543","https://openalex.org/W2102963498","https://openalex.org/W2105012045","https://openalex.org/W2108360982","https://openalex.org/W2119962968","https://openalex.org/W2122646361","https://openalex.org/W2144182447","https://openalex.org/W2170755382","https://openalex.org/W2172368975","https://openalex.org/W2305848008","https://openalex.org/W2376327285","https://openalex.org/W2392897035","https://openalex.org/W2607421718","https://openalex.org/W2809438551","https://openalex.org/W2901560888","https://openalex.org/W2981506493","https://openalex.org/W2982416429","https://openalex.org/W4211009159","https://openalex.org/W4255833381","https://openalex.org/W6796843916"],"related_works":["https://openalex.org/W2353179089","https://openalex.org/W2392963705","https://openalex.org/W2107349454","https://openalex.org/W2382278777","https://openalex.org/W2499612753","https://openalex.org/W3111802945","https://openalex.org/W2946096271","https://openalex.org/W2295423552","https://openalex.org/W3107369729","https://openalex.org/W2131323062"],"abstract_inverted_index":{"Abstract":[0],"The":[1,34,83,97,139],"rough":[2,22,31,36,68,111],"set":[3,23,32,37],"theory":[4,38],"is":[5,39,119,132,144],"a":[6],"powerful":[7],"numerical":[8],"model":[9],"used":[10],"to":[11,169],"handle":[12],"the":[13,28,100,110,136,142,166],"impreciseness":[14],"and":[15,54,80,108,124,146,159,173],"ambiguity":[16],"of":[17,73,76,99,141],"data.":[18],"Many":[19],"existing":[20,149],"multigranulation":[21,29,35,67],"models":[24],"were":[25,62],"derived":[26,104],"from":[27,165],"decision-theoretic":[30],"framework.":[33],"very":[40],"desirable":[41],"in":[42,70,91],"many":[43],"practical":[44],"applications":[45],"such":[46,154],"as":[47,155],"high-dimensional":[48],"knowledge":[49],"discovery,":[50],"distributional":[51],"information":[52],"systems,":[53],"multisource":[55],"data":[56,93],"processing.":[57],"So":[58],"far":[59],"research":[60],"works":[61],"carried":[63],"out":[64],"only":[65],"for":[66],"sets":[69],"extraction,":[71],"selection":[72],"features,":[74],"reduction":[75],"data,":[77],"decision":[78],"rules,":[79],"pattern":[81],"extraction.":[82],"proposed":[84],"approach":[85],"mainly":[86],"focuses":[87],"on":[88,121,135],"anomaly":[89],"detection":[90,151],"qualitative":[92],"with":[94,115,148],"multiple":[95],"granules.":[96],"approximations":[98],"dataset":[101],"will":[102],"be":[103],"through":[105],"multiequivalence":[106],"relation,":[107],"then,":[109],"set-based":[112],"entropy":[113],"measure":[114],"weighted":[116],"density":[117],"method":[118],"applied":[120],"every":[122],"object":[123],"attribute.":[125],"For":[126],"detecting":[127],"outliers,":[128],"threshold":[129],"value":[130],"fixation":[131],"performed":[133],"based":[134],"estimated":[137],"weight.":[138],"performance":[140],"algorithm":[143],"evaluated":[145],"compared":[147],"outlier":[150],"algorithms.":[152],"Datasets":[153],"breast":[156],"cancer,":[157],"chess,":[158],"car":[160],"evaluation":[161],"have":[162],"been":[163],"taken":[164],"UCI":[167],"repository":[168],"prove":[170],"its":[171],"efficiency":[172],"performance.":[174]},"counts_by_year":[{"year":2023,"cited_by_count":3}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2022-05-05T00:00:00"}
