{"id":"https://openalex.org/W4392845515","doi":"https://doi.org/10.1145/3625007.3627497","title":"CFAR++: Enhancing Rule Based Classifier","display_name":"CFAR++: Enhancing Rule Based Classifier","publication_year":2023,"publication_date":"2023-11-06","ids":{"openalex":"https://openalex.org/W4392845515","doi":"https://doi.org/10.1145/3625007.3627497"},"language":"en","primary_location":{"id":"doi:10.1145/3625007.3627497","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3625007.3627497","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3625007.3627497","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Advances in Social Networks Analysis and Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3625007.3627497","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026455832","display_name":"Md Rayhan Kabir","orcid":"https://orcid.org/0009-0002-6898-1805"},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Md Rayhan Kabir","raw_affiliation_strings":["University of Alberta, Edmonton, Alberta, Canada"],"raw_orcid":"https://orcid.org/0009-0002-6898-1805","affiliations":[{"raw_affiliation_string":"University of Alberta, Edmonton, Alberta, Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009494137","display_name":"Seeratpal Jaura","orcid":"https://orcid.org/0009-0003-8960-3747"},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Seeratpal Jaura","raw_affiliation_strings":["University of Alberta, Edmonton, Alberta, Canada"],"raw_orcid":"https://orcid.org/0009-0003-8960-3747","affiliations":[{"raw_affiliation_string":"University of Alberta, Edmonton, Alberta, Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053745515","display_name":"Osmar R. Za\u0131\u0308ane","orcid":"https://orcid.org/0000-0002-0060-5988"},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Osmar R. Zaiane","raw_affiliation_strings":["Alberta Machine Intelligence Institute, University of Alberta, Edmonton, Alberta, Canada"],"raw_orcid":"https://orcid.org/0000-0002-0060-5988","affiliations":[{"raw_affiliation_string":"Alberta Machine Intelligence Institute, University of Alberta, Edmonton, Alberta, Canada","institution_ids":["https://openalex.org/I154425047"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5026455832"],"corresponding_institution_ids":["https://openalex.org/I154425047"],"apc_list":null,"apc_paid":null,"fwci":0.4484,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.74317952,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"692","last_page":"697"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9991000294685364,"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":0.9991000294685364,"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/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.9925000071525574,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9915000200271606,"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/computer-science","display_name":"Computer science","score":0.6854288578033447},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5432443022727966},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.531455397605896},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4292669892311096},{"id":"https://openalex.org/keywords/rule-based-system","display_name":"Rule-based system","score":0.42049431800842285},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33684176206588745}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6854288578033447},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5432443022727966},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.531455397605896},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4292669892311096},{"id":"https://openalex.org/C149271511","wikidata":"https://www.wikidata.org/wiki/Q1417149","display_name":"Rule-based system","level":2,"score":0.42049431800842285},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33684176206588745}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3625007.3627497","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3625007.3627497","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3625007.3627497","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Advances in Social Networks Analysis and Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3625007.3627497","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3625007.3627497","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3625007.3627497","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Advances in Social Networks Analysis and Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4392845515.pdf","grobid_xml":"https://content.openalex.org/works/W4392845515.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W1520890006","https://openalex.org/W1873332500","https://openalex.org/W2014249115","https://openalex.org/W2031648200","https://openalex.org/W2061433096","https://openalex.org/W2086744970","https://openalex.org/W2110893883","https://openalex.org/W2122094726","https://openalex.org/W2154642793","https://openalex.org/W2765547873","https://openalex.org/W2911965020","https://openalex.org/W3089427591","https://openalex.org/W4285088909","https://openalex.org/W4300874750","https://openalex.org/W4360765013","https://openalex.org/W6636721509","https://openalex.org/W6848732804"],"related_works":["https://openalex.org/W3188962172","https://openalex.org/W2772917594","https://openalex.org/W4306742369","https://openalex.org/W4303457083","https://openalex.org/W2131146434","https://openalex.org/W2951359407","https://openalex.org/W4376623224","https://openalex.org/W3136979370","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Over":[0],"the":[1,43,54,71,75,91,103,116,119,126,129,147,172,176],"last":[2],"few":[3],"years,":[4],"associative":[5,37,87,105,131],"classifiers":[6,19,38,52,106],"have":[7,39,46,80],"shown":[8,47],"massive":[9],"success":[10],"in":[11,150],"mining":[12],"patterns":[13],"using":[14,94,137],"association":[15,99],"rules.":[16,76,101],"These":[17],"rule-based":[18],"offer":[20],"a":[21,27,151,180],"level":[22],"of":[23,56,74,93,118,128,163],"human":[24],"interpretability,":[25],"addressing":[26],"common":[28],"concern":[29],"stemming":[30],"from":[31],"several":[32],"deep":[33],"learning":[34],"models.":[35],"Various":[36],"been":[40],"proposed":[41],"over":[42],"past":[44],"that":[45,89],"state-of-the-art":[48],"performance.":[49],"However,":[50],"those":[51,66],"suffer":[53],"limitation":[55,83],"requiring":[57],"parametric":[58],"values":[59],"which":[60],"vary":[61],"across":[62],"different":[63],"datasets.":[64],"Furthermore,":[65],"frameworks":[67],"do":[68],"not":[69],"consider":[70],"statistical":[72,95],"significance":[73,96],"Recently,":[77],"some":[78],"works":[79],"addressed":[81],"this":[82,122],"by":[84,115],"proposing":[85],"an":[86],"classifier":[88],"incorporates":[90],"idea":[92],"to":[97,140],"mine":[98],"classification":[100,132],"Though":[102],"recent":[104,130],"show":[107],"good":[108],"performance,":[109],"their":[110],"performance":[111],"is":[112],"greatly":[113],"affected":[114],"dimension":[117],"data.":[120],"In":[121],"study,":[123,169],"we":[124,170],"explore":[125],"weakness":[127],"models":[133,139,149],"and":[134,186],"experiment":[135],"with":[136,179],"ensemble":[138,148,177],"overcome":[141],"such":[142],"limitations,":[143],"particularly":[144],"on":[145,175],"aggregating":[146],"concise":[152],"but":[153],"effective":[154],"predictor.":[155],"We":[156],"use":[157],"10":[158],"UCI":[159],"datasets":[160],"for":[161],"evaluation":[162],"our":[164,168],"new":[165],"approach.":[166],"From":[167],"find":[171],"results":[173],"based":[174],"model":[178],"delayed":[181],"pruning":[182],"are":[183],"very":[184],"competitive":[185],"can":[187],"better":[188],"handle":[189],"large":[190],"dimensional":[191],"data":[192],"spaces.":[193]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}