{"id":"https://openalex.org/W2052147532","doi":"https://doi.org/10.1142/s0218001414510070","title":"AN INFORMATION-THEORETIC FILTER METHOD FOR FEATURE WEIGHTING IN NAIVE BAYES","display_name":"AN INFORMATION-THEORETIC FILTER METHOD FOR FEATURE WEIGHTING IN NAIVE BAYES","publication_year":2014,"publication_date":"2014-06-23","ids":{"openalex":"https://openalex.org/W2052147532","doi":"https://doi.org/10.1142/s0218001414510070","mag":"2052147532"},"language":"en","primary_location":{"id":"doi:10.1142/s0218001414510070","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001414510070","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","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/A5072515962","display_name":"Chang\u2010Hwan Lee","orcid":"https://orcid.org/0000-0003-3221-1171"},"institutions":[{"id":"https://openalex.org/I205490536","display_name":"Dongguk University","ror":"https://ror.org/057q6n778","country_code":"KR","type":"education","lineage":["https://openalex.org/I205490536"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"CHANG-HWAN LEE","raw_affiliation_strings":["Department of Information and Communications, Dongguk University, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information and Communications, Dongguk University, Seoul, Korea","institution_ids":["https://openalex.org/I205490536"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5072515962"],"corresponding_institution_ids":["https://openalex.org/I205490536"],"apc_list":null,"apc_paid":null,"fwci":0.8456,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.81372804,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"28","issue":"05","first_page":"1451007","last_page":"1451007"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9984999895095825,"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":0.9984999895095825,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9976999759674072,"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":0.9973000288009644,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.7135875225067139},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7102076411247253},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.700408935546875},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.6844073534011841},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6406764388084412},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6306531429290771},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6023364067077637},{"id":"https://openalex.org/keywords/simplicity","display_name":"Simplicity","score":0.5012936592102051},{"id":"https://openalex.org/keywords/bayesian-programming","display_name":"Bayesian programming","score":0.4936724603176117},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.454293429851532},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45246267318725586},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4326225519180298},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4221428632736206},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36768969893455505},{"id":"https://openalex.org/keywords/bayesian-statistics","display_name":"Bayesian statistics","score":0.2932989299297333},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.10146859288215637}],"concepts":[{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.7135875225067139},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7102076411247253},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.700408935546875},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6844073534011841},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6406764388084412},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6306531429290771},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6023364067077637},{"id":"https://openalex.org/C2776372474","wikidata":"https://www.wikidata.org/wiki/Q508291","display_name":"Simplicity","level":2,"score":0.5012936592102051},{"id":"https://openalex.org/C127043819","wikidata":"https://www.wikidata.org/wiki/Q16243608","display_name":"Bayesian programming","level":5,"score":0.4936724603176117},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.454293429851532},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45246267318725586},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4326225519180298},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4221428632736206},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36768969893455505},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.2932989299297333},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.10146859288215637},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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},{"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:10.1142/s0218001414510070","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001414510070","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1689445748","https://openalex.org/W1817561967","https://openalex.org/W1827261456","https://openalex.org/W1853307972","https://openalex.org/W1965555277","https://openalex.org/W1975299065","https://openalex.org/W2017337590","https://openalex.org/W2019439081","https://openalex.org/W2092478087","https://openalex.org/W2118978333","https://openalex.org/W2125055259","https://openalex.org/W2133990480","https://openalex.org/W2140785063","https://openalex.org/W2911964244","https://openalex.org/W3120740533"],"related_works":["https://openalex.org/W1987683558","https://openalex.org/W2172238468","https://openalex.org/W2363389114","https://openalex.org/W2171665309","https://openalex.org/W2028042040","https://openalex.org/W4367330402","https://openalex.org/W2373790322","https://openalex.org/W2147409005","https://openalex.org/W2145310314","https://openalex.org/W2949661619"],"abstract_inverted_index":{"In":[0,35],"spite":[1],"of":[2,31,52,66,73],"its":[3],"simplicity,":[4],"naive":[5,32,57],"Bayesian":[6,33,58],"learning":[7],"has":[8],"been":[9],"widely":[10],"used":[11],"in":[12,56],"many":[13],"data":[14],"mining":[15],"applications.":[16],"However,":[17],"the":[18,29,50,53],"unrealistic":[19],"assumption":[20],"that":[21,43,65],"all":[22],"features":[23,54],"are":[24],"equally":[25],"important":[26],"negatively":[27],"impacts":[28],"performance":[30,61],"learning.":[34,59],"this":[36],"paper,":[37],"we":[38],"propose":[39],"a":[40,45,71],"new":[41],"method":[42],"uses":[44],"Kullback\u2013Leibler":[46],"measure":[47],"to":[48,64],"calculate":[49],"weights":[51],"analyzed":[55],"Its":[60],"is":[62],"compared":[63],"other":[67],"state-of-the-art":[68],"methods":[69],"over":[70],"number":[72],"datasets.":[74]},"counts_by_year":[{"year":2015,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
