{"id":"https://openalex.org/W4387914368","doi":"https://doi.org/10.1109/codit58514.2023.10284408","title":"A Fully Bayesian Inference Approach for Multivariate McDonald's Beta Mixture Model with Feature Selection","display_name":"A Fully Bayesian Inference Approach for Multivariate McDonald's Beta Mixture Model with Feature Selection","publication_year":2023,"publication_date":"2023-07-03","ids":{"openalex":"https://openalex.org/W4387914368","doi":"https://doi.org/10.1109/codit58514.2023.10284408"},"language":"en","primary_location":{"id":"doi:10.1109/codit58514.2023.10284408","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/codit58514.2023.10284408","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 9th International Conference on Control, Decision and Information Technologies (CoDIT)","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/A5092105218","display_name":"Darya Forouzanfar","orcid":"https://orcid.org/0000-0003-4837-4338"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Darya Forouzanfar","raw_affiliation_strings":["Concordia University, Concordia Institute for Information Systems Engineering,Montreal,Canada","Concordia University, Concordia Institute for Information Systems Engineering, Montreal, Canada"],"affiliations":[{"raw_affiliation_string":"Concordia University, Concordia Institute for Information Systems Engineering,Montreal,Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Concordia University, Concordia Institute for Information Systems Engineering, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091833816","display_name":"Narges Manouchehri","orcid":"https://orcid.org/0000-0002-3011-5162"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]},{"id":"https://openalex.org/I28166907","display_name":"Karolinska Institutet","ror":"https://ror.org/056d84691","country_code":"SE","type":"education","lineage":["https://openalex.org/I28166907"]}],"countries":["CA","SE"],"is_corresponding":false,"raw_author_name":"Narges Manouchehri","raw_affiliation_strings":["Karolinska Institute,Algorithmic Dynamics Lab, Unit of Computational Medicine,Stockholm,Sweden,171 77","Concordia University, Concordia Institute for Information Systems Engineering, Montreal, Canada"],"affiliations":[{"raw_affiliation_string":"Karolinska Institute,Algorithmic Dynamics Lab, Unit of Computational Medicine,Stockholm,Sweden,171 77","institution_ids":["https://openalex.org/I28166907"]},{"raw_affiliation_string":"Concordia University, Concordia Institute for Information Systems Engineering, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090600716","display_name":"Nizar Bouguila","orcid":"https://orcid.org/0000-0001-7224-7940"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Nizar Bouguila","raw_affiliation_strings":["Concordia University, Concordia Institute for Information Systems Engineering,Montreal,Canada","Concordia University, Concordia Institute for Information Systems Engineering, Montreal, Canada"],"affiliations":[{"raw_affiliation_string":"Concordia University, Concordia Institute for Information Systems Engineering,Montreal,Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Concordia University, Concordia Institute for Information Systems Engineering, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5092105218"],"corresponding_institution_ids":["https://openalex.org/I60158472"],"apc_list":null,"apc_paid":null,"fwci":0.1751,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57042152,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"30","issue":null,"first_page":"2055","last_page":"2060"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9997000098228455,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9997000098228455,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.963699996471405,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9598000049591064,"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/mixture-model","display_name":"Mixture model","score":0.7962949275970459},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6785287857055664},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6696518063545227},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6688088178634644},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.6266401410102844},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.6235501170158386},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5620682835578918},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5456446409225464},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.503161609172821},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.502286434173584},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.4795819818973541},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.45848509669303894},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4388807713985443},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.43086305260658264},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41452547907829285},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.2857159376144409}],"concepts":[{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.7962949275970459},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6785287857055664},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6696518063545227},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6688088178634644},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.6266401410102844},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.6235501170158386},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5620682835578918},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5456446409225464},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.503161609172821},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.502286434173584},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.4795819818973541},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.45848509669303894},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4388807713985443},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.43086305260658264},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41452547907829285},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.2857159376144409},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/codit58514.2023.10284408","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/codit58514.2023.10284408","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 9th International Conference on Control, Decision and Information Technologies (CoDIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W29489373","https://openalex.org/W134960717","https://openalex.org/W967827082","https://openalex.org/W1995139576","https://openalex.org/W2041993031","https://openalex.org/W2052085418","https://openalex.org/W2080104526","https://openalex.org/W2082697323","https://openalex.org/W2124386111","https://openalex.org/W2151103935","https://openalex.org/W2462190434","https://openalex.org/W2793631173","https://openalex.org/W2898045070","https://openalex.org/W2907500525","https://openalex.org/W2969565428","https://openalex.org/W3086637446","https://openalex.org/W3093801655","https://openalex.org/W3159316835","https://openalex.org/W3211963602","https://openalex.org/W3216701780","https://openalex.org/W4313564081","https://openalex.org/W4379620314","https://openalex.org/W6605479355"],"related_works":["https://openalex.org/W3146360815","https://openalex.org/W1570787356","https://openalex.org/W208520381","https://openalex.org/W2517377710","https://openalex.org/W2088304067","https://openalex.org/W1853799209","https://openalex.org/W1992295166","https://openalex.org/W2025931908","https://openalex.org/W1998035802","https://openalex.org/W2143508933"],"abstract_inverted_index":{"Mixture":[0],"models":[1],"are":[2],"widely":[3],"used":[4],"in":[5,23],"unsupervised":[6,97],"machine":[7],"learning":[8,98],"applications":[9],"where":[10],"annotating":[11],"a":[12,39,64],"large":[13],"amount":[14],"of":[15,43,85,95],"data":[16],"is":[17,102,123],"not":[18],"feasible.":[19],"They":[20],"have":[21],"succeeded":[22],"various":[24],"real-world":[25],"problems,":[26],"including":[27,107],"medical":[28],"applications,":[29,106],"human":[30,113],"activity":[31,114],"recognition,":[32],"and":[33,55,93,112],"anomaly":[34],"detection.":[35],"This":[36,79],"paper":[37],"proposes":[38],"fully":[40],"Bayesian":[41],"analysis":[42,111],"the":[44,71,82,86,91,96,129],"multivariate":[45],"McDonald's":[46],"Beta":[47],"mixture":[48,77,131],"model":[49,132],"(McDBMM)":[50],"using":[51],"Gibbs":[52],"sampling":[53],"method":[54,122],"Metropolis-Hastings":[56],"to":[57,128],"estimate":[58],"parameters.":[59],"In":[60],"addition,":[61],"we":[62],"integrated":[63],"feature":[65],"selection":[66,84],"technique":[67],"which":[68],"simultaneously":[69],"determines":[70],"most":[72,87],"relevant":[73,88],"features":[74],"for":[75,81],"our":[76,120],"model.":[78],"allows":[80],"simultaneous":[83],"features,":[89],"improving":[90],"accuracy":[92],"efficiency":[94],"process.":[99],"Our":[100],"approach":[101],"evaluated":[103],"on":[104],"challenging":[105],"lung":[108],"cancer":[109],"image":[110],"recognition.":[115],"Experimental":[116],"results":[117],"indicate":[118],"that":[119],"proposed":[121],"an":[124],"effective":[125],"solution":[126],"compared":[127],"Gaussian":[130],"(GMM).":[133]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}
