{"id":"https://openalex.org/W2062909732","doi":"https://doi.org/10.1109/nabic.2009.5393687","title":"An associative classifier using weighted association rule","display_name":"An associative classifier using weighted association rule","publication_year":2009,"publication_date":"2009-01-01","ids":{"openalex":"https://openalex.org/W2062909732","doi":"https://doi.org/10.1109/nabic.2009.5393687","mag":"2062909732"},"language":"en","primary_location":{"id":"doi:10.1109/nabic.2009.5393687","is_oa":false,"landing_page_url":"https://doi.org/10.1109/nabic.2009.5393687","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 World Congress on Nature &amp; Biologically Inspired Computing (NaBIC)","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/A5113028257","display_name":"Sunita Soni","orcid":null},"institutions":[{"id":"https://openalex.org/I4210121466","display_name":"Indian Institute of Technology Bhilai","ror":"https://ror.org/02sscsx71","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210121466"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Sunita Soni","raw_affiliation_strings":["Bhilai Institute of Technology, Chhattisgarh, India","Bhilai Institute of Technology, Durg-491 001, Chhattisgarh, India"],"affiliations":[{"raw_affiliation_string":"Bhilai Institute of Technology, Chhattisgarh, India","institution_ids":["https://openalex.org/I4210121466"]},{"raw_affiliation_string":"Bhilai Institute of Technology, Durg-491 001, Chhattisgarh, India","institution_ids":["https://openalex.org/I4210121466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109243243","display_name":"Jyothi Pillai","orcid":"https://orcid.org/0000-0003-2477-1398"},"institutions":[{"id":"https://openalex.org/I4210121466","display_name":"Indian Institute of Technology Bhilai","ror":"https://ror.org/02sscsx71","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210121466"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Jyothi Pillai","raw_affiliation_strings":["Bhilai Institute of Technology, Chhattisgarh, India","Bhilai Institute of Technology, Durg-491 001, Chhattisgarh, India"],"affiliations":[{"raw_affiliation_string":"Bhilai Institute of Technology, Chhattisgarh, India","institution_ids":["https://openalex.org/I4210121466"]},{"raw_affiliation_string":"Bhilai Institute of Technology, Durg-491 001, Chhattisgarh, India","institution_ids":["https://openalex.org/I4210121466"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004099006","display_name":"O. P. Vyas","orcid":"https://orcid.org/0000-0001-5014-8837"},"institutions":[{"id":"https://openalex.org/I26072440","display_name":"Indian Institute of Information Technology Allahabad","ror":"https://ror.org/03rgjt374","country_code":"IN","type":"education","lineage":["https://openalex.org/I26072440"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"O.P. Vyas","raw_affiliation_strings":["Indian Institute of Information Technology, Allahabad, Allahabad, India","Indian Institute of Information Technology, Allahabad, Deoghat Jhalwa, India"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Information Technology, Allahabad, Allahabad, India","institution_ids":["https://openalex.org/I26072440"]},{"raw_affiliation_string":"Indian Institute of Information Technology, Allahabad, Deoghat Jhalwa, India","institution_ids":["https://openalex.org/I26072440"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5113028257"],"corresponding_institution_ids":["https://openalex.org/I4210121466"],"apc_list":null,"apc_paid":null,"fwci":4.4418,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.94987944,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"5","issue":null,"first_page":"1492","last_page":"1496"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9955000281333923,"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":0.9866999983787537,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/association-rule-learning","display_name":"Association rule learning","score":0.8128625154495239},{"id":"https://openalex.org/keywords/associative-property","display_name":"Associative property","score":0.8022716045379639},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7799819707870483},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6764950752258301},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.6647279858589172},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6619499921798706},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.6140285730361938},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5654529333114624},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5255105495452881},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3727981746196747},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12335336208343506},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.10540628433227539}],"concepts":[{"id":"https://openalex.org/C193524817","wikidata":"https://www.wikidata.org/wiki/Q386780","display_name":"Association rule learning","level":2,"score":0.8128625154495239},{"id":"https://openalex.org/C159423971","wikidata":"https://www.wikidata.org/wiki/Q177251","display_name":"Associative property","level":2,"score":0.8022716045379639},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7799819707870483},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6764950752258301},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6647279858589172},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6619499921798706},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6140285730361938},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5654529333114624},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5255105495452881},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3727981746196747},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12335336208343506},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.10540628433227539},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/nabic.2009.5393687","is_oa":false,"landing_page_url":"https://doi.org/10.1109/nabic.2009.5393687","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 World Congress on Nature &amp; Biologically Inspired Computing (NaBIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7599999904632568,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1484413656","https://openalex.org/W1506285740","https://openalex.org/W1550084988","https://openalex.org/W1623342295","https://openalex.org/W1927392184","https://openalex.org/W1966666559","https://openalex.org/W1987191780","https://openalex.org/W2034607334","https://openalex.org/W2057858164","https://openalex.org/W2094770144","https://openalex.org/W2098268836","https://openalex.org/W2110121091","https://openalex.org/W2119336358","https://openalex.org/W2129095048","https://openalex.org/W2138061404","https://openalex.org/W2154642793","https://openalex.org/W2167681385","https://openalex.org/W4253376897","https://openalex.org/W6628750762","https://openalex.org/W6682837551","https://openalex.org/W6684495928"],"related_works":["https://openalex.org/W1533029210","https://openalex.org/W4389954502","https://openalex.org/W2771255398","https://openalex.org/W2930428186","https://openalex.org/W3200027047","https://openalex.org/W4385770464","https://openalex.org/W2148338580","https://openalex.org/W4224262160","https://openalex.org/W3120363735","https://openalex.org/W4214820172"],"abstract_inverted_index":{"New":[0],"classification":[1,9,23,69],"approach":[2],"that":[3,56,123,170],"use":[4],"association":[5,126,175],"rule":[6,127,176],"mining":[7,128],"and":[8,105],"has":[10],"become":[11],"a":[12,40,118,162],"significant":[13],"tool":[14],"for":[15],"knowledge":[16],"discovery.":[17],"An":[18],"important":[19],"advantage":[20,172],"of":[21,45,60,100,109,173],"these":[22],"systems":[24],"is":[25,58,103],"that,":[26],"using":[27],"Association":[28],"Rule":[29],"Mining":[30],"(ARM)":[31],"they":[32],"are":[33,73,90],"able":[34],"to":[35,76,152,156,165,186],"examine":[36],"several":[37],"features":[38],"at":[39],"time.":[41],"While":[42],"other":[43],"state":[44],"art":[46],"methods":[47],"like":[48],"decision":[49],"tree":[50],"or":[51],"naive":[52],"bayes":[53],"classifiers":[54,72],"consider":[55],"feature":[57],"independent":[59],"each":[61],"other.":[62],"Many":[63],"applications":[64,77],"can":[65,149,180],"benefit":[66],"from":[67],"good":[68],"model.":[70],"Associative":[71],"especially":[74],"fit":[75],"where":[78,96],"the":[79,83,97,101,107,110,143,188],"model":[80,102,133,164,179],"may":[81],"assist":[82],"domain":[84,185],"experts":[85],"in":[86,141,183],"their":[87,157],"decisions.":[88],"There":[89],"many":[91],"domains":[92],"such":[93],"as":[94],"medical,":[95],"maximum":[98],"accuracy":[99,108],"desired":[104],"hence":[106],"associative":[111,168],"classifiers.":[112],"In":[113,130],"this":[114],"paper,":[115],"we":[116],"propose":[117,161],"new":[119,167],"framework":[120],"(associative":[121],"classifier)":[122],"uses":[124],"weighted":[125,174],"(WARM).":[129],"any":[131,184],"prediction":[132,189],"all":[134],"attributes":[135,154],"do":[136],"not":[137],"have":[138],"same":[139],"importance":[140],"predicting":[142,158],"class":[144],"label.":[145],"So":[146],"different":[147,153],"weights":[148],"be":[150,181],"assigned":[151],"according":[155],"capability.":[159],"We":[160],"theoretical":[163],"introduce":[166],"classifier":[169],"takes":[171],"mining.":[177],"The":[178],"applied":[182],"improve":[187],"accuracy.":[190]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
