{"id":"https://openalex.org/W4293868370","doi":"https://doi.org/10.1109/siu55565.2022.9864833","title":"A Dependent Feature Weighting Filter for Naive Bayes Classifier","display_name":"A Dependent Feature Weighting Filter for Naive Bayes Classifier","publication_year":2022,"publication_date":"2022-05-15","ids":{"openalex":"https://openalex.org/W4293868370","doi":"https://doi.org/10.1109/siu55565.2022.9864833"},"language":"en","primary_location":{"id":"doi:10.1109/siu55565.2022.9864833","is_oa":false,"landing_page_url":"https://doi.org/10.1109/siu55565.2022.9864833","pdf_url":null,"source":{"id":"https://openalex.org/S4363607818","display_name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","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/A5061533506","display_name":"Gieliz Chatip","orcid":null},"institutions":[{"id":"https://openalex.org/I4101805","display_name":"Y\u0131ld\u0131z Technical University","ror":"https://ror.org/0547yzj13","country_code":"TR","type":"education","lineage":["https://openalex.org/I4101805"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Gieliz Chatip","raw_affiliation_strings":["Yildiz Technical University (YTU),Computer Engineering,Istanbul,Turkey","Computer Engineering, Yildiz Technical University (YTU), Istanbul, Turkey"],"affiliations":[{"raw_affiliation_string":"Yildiz Technical University (YTU),Computer Engineering,Istanbul,Turkey","institution_ids":["https://openalex.org/I4101805"]},{"raw_affiliation_string":"Computer Engineering, Yildiz Technical University (YTU), Istanbul, Turkey","institution_ids":["https://openalex.org/I4101805"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030136088","display_name":"Ferkan Y\u0131lmaz","orcid":"https://orcid.org/0000-0001-6502-8280"},"institutions":[{"id":"https://openalex.org/I4101805","display_name":"Y\u0131ld\u0131z Technical University","ror":"https://ror.org/0547yzj13","country_code":"TR","type":"education","lineage":["https://openalex.org/I4101805"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Ferkan Yilmaz","raw_affiliation_strings":["Yildiz Technical University (YTU),Electronics and Communications Engineering,Istanbul,Turkey","Electronics and Communications Engineering, Yildiz Technical University (YTU), Istanbul, Turkey"],"affiliations":[{"raw_affiliation_string":"Yildiz Technical University (YTU),Electronics and Communications Engineering,Istanbul,Turkey","institution_ids":["https://openalex.org/I4101805"]},{"raw_affiliation_string":"Electronics and Communications Engineering, Yildiz Technical University (YTU), Istanbul, Turkey","institution_ids":["https://openalex.org/I4101805"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5061533506"],"corresponding_institution_ids":["https://openalex.org/I4101805"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08705489,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"7","issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9954000115394592,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9954000115394592,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9815000295639038,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.980400025844574,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.803377091884613},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.7485091686248779},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6925215721130371},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6683297753334045},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6458062529563904},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6100687384605408},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.49682310223579407},{"id":"https://openalex.org/keywords/conditional-independence","display_name":"Conditional independence","score":0.49103403091430664},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4524584412574768},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44126972556114197},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4244720935821533},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.41608932614326477},{"id":"https://openalex.org/keywords/independence","display_name":"Independence (probability theory)","score":0.41062217950820923},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27681422233581543},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.19483837485313416},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.17014294862747192}],"concepts":[{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.803377091884613},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.7485091686248779},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6925215721130371},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6683297753334045},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6458062529563904},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6100687384605408},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.49682310223579407},{"id":"https://openalex.org/C79772020","wikidata":"https://www.wikidata.org/wiki/Q5159264","display_name":"Conditional independence","level":2,"score":0.49103403091430664},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4524584412574768},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44126972556114197},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4244720935821533},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.41608932614326477},{"id":"https://openalex.org/C35651441","wikidata":"https://www.wikidata.org/wiki/Q625303","display_name":"Independence (probability theory)","level":2,"score":0.41062217950820923},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27681422233581543},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.19483837485313416},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17014294862747192},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/siu55565.2022.9864833","is_oa":false,"landing_page_url":"https://doi.org/10.1109/siu55565.2022.9864833","pdf_url":null,"source":{"id":"https://openalex.org/S4363607818","display_name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 30th Signal Processing and Communications Applications Conference (SIU)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W49057944","https://openalex.org/W1530964327","https://openalex.org/W1542270631","https://openalex.org/W1817561967","https://openalex.org/W1853307972","https://openalex.org/W1912123407","https://openalex.org/W1975299065","https://openalex.org/W2019439081","https://openalex.org/W2032026767","https://openalex.org/W2108626773","https://openalex.org/W2140190241","https://openalex.org/W2142827986","https://openalex.org/W2282289695","https://openalex.org/W2363768991","https://openalex.org/W2495603755","https://openalex.org/W2803413127","https://openalex.org/W3135028703","https://openalex.org/W3213915676","https://openalex.org/W4242097849","https://openalex.org/W6640114639","https://openalex.org/W6643985368","https://openalex.org/W6655311804","https://openalex.org/W6676351932","https://openalex.org/W6707641287"],"related_works":["https://openalex.org/W4367336074","https://openalex.org/W3154045278","https://openalex.org/W4379620016","https://openalex.org/W4393666307","https://openalex.org/W3210764983","https://openalex.org/W4393443811","https://openalex.org/W4367335949","https://openalex.org/W3089416646","https://openalex.org/W4396816114","https://openalex.org/W4380048833"],"abstract_inverted_index":{"Naive":[0],"Bayes":[1],"(NB)":[2],"classification":[3],"is":[4],"one":[5],"of":[6,30,99,107,132,137],"the":[7,42,96,103,116,134,155,166],"most":[8],"extensively":[9],"used":[10],"algorithms":[11],"in":[12,65],"data":[13],"mining":[14],"and":[15,23,46,49,54,77,79,109,143,169],"machine":[16],"learning":[17],"due":[18,101],"to":[19,40,71,102,114,165],"its":[20],"high":[21],"efficiency":[22],"structural":[24],"simplicity":[25],"based":[26],"on":[27],"conditional":[28,117],"independence":[29],"attributes.":[31],"In":[32],"this":[33],"paper,":[34],"we":[35,93,122],"present":[36],"a":[37,81,120,124,130],"dependence":[38,43,105],"metric":[39],"quantify":[41],"among":[44],"attributes":[45,48,60],"class":[47],"propose":[50,80,123],"feature-feature":[51],"significance":[52],"(FFS)":[53],"feature-class":[55],"significance(FCS)to":[56],"discover":[57],"highly":[58],"predictive":[59,63],"over":[61],"less":[62],"ones":[64],"NB":[66,87,128,156,168],"classification.":[67,88],"We":[68],"show":[69,153],"how":[70],"get":[72],"feature":[73,84,112,172],"weights":[74],"from":[75],"FFS":[76],"FCS":[78],"novel":[82],"dependent":[83],"weighted":[85],"(DFW)":[86],"To":[89],"increase":[90],"performance":[91,148],"further,":[92],"recommend":[94],"clustering":[95],"random":[97,138],"sample":[98],"interest":[100],"non-homogeneous":[104],"nature":[106],"features,":[108],"then":[110,144],"using":[111],"weighting":[113,133,173],"alleviate":[115],"independence.":[118],"As":[119],"consequence,":[121],"cluster-based":[125],"DFW":[126,135,158],"(CDFW)":[127],"as":[129],"result":[131],"filters":[136],"sub-samples":[139],"by":[140],"their":[141],"accuracy":[142],"merging":[145],"them":[146],"for":[147],"augmentation.":[149],"The":[150],"experimental":[151],"results":[152,162],"that":[154],"with":[157],"filter":[159],"provides":[160],"good":[161],"when":[163],"compared":[164],"conventional":[167],"all":[170],"other":[171],"techniques.":[174]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
