{"id":"https://openalex.org/W4363675928","doi":"https://doi.org/10.1080/03610918.2023.2196384","title":"Unsupervised methods for size and shape","display_name":"Unsupervised methods for size and shape","publication_year":2023,"publication_date":"2023-04-09","ids":{"openalex":"https://openalex.org/W4363675928","doi":"https://doi.org/10.1080/03610918.2023.2196384"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2023.2196384","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2023.2196384","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","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/A5066386329","display_name":"Jerfson Bruno do Nascimento Hon\u00f3rio","orcid":"https://orcid.org/0000-0002-7627-8266"},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Jerfson Bruno do Nascimento Hon\u00f3rio","raw_affiliation_strings":["Department of Statistics, Federal University of Pernambuco, CCEN, University City, Recife, Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Federal University of Pernambuco, CCEN, University City, Recife, Brazil","institution_ids":["https://openalex.org/I25112270"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035010764","display_name":"Get\u00falio Jos\u00e9 Amorim do Amaral","orcid":"https://orcid.org/0000-0002-4129-1508"},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Get\u00falio Jos\u00e9 Amorim do Amaral","raw_affiliation_strings":["Department of Statistics, Federal University of Pernambuco, CCEN, University City, Recife, Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Federal University of Pernambuco, CCEN, University City, Recife, Brazil","institution_ids":["https://openalex.org/I25112270"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5066386329"],"corresponding_institution_ids":["https://openalex.org/I25112270"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09142516,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"53","issue":"11","first_page":"5643","last_page":"5658"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12417","display_name":"Morphological variations and asymmetry","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/2608","display_name":"Geometry and Topology"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12417","display_name":"Morphological variations and asymmetry","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/2608","display_name":"Geometry and Topology"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9772999882698059,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10057","display_name":"Face and Expression Recognition","score":0.9473999738693237,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/boosting","display_name":"Boosting (machine learning)","score":0.7644814252853394},{"id":"https://openalex.org/keywords/centroid","display_name":"Centroid","score":0.6931105852127075},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6729623079299927},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5062467455863953},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48446106910705566},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4472914934158325},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3980012536048889},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3760230541229248}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7644814252853394},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.6931105852127075},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6729623079299927},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5062467455863953},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48446106910705566},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4472914934158325},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3980012536048889},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3760230541229248}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2023.2196384","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2023.2196384","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W33507944","https://openalex.org/W334078648","https://openalex.org/W1552730281","https://openalex.org/W2033403400","https://openalex.org/W2052135370","https://openalex.org/W2064771454","https://openalex.org/W2091804476","https://openalex.org/W2127218421","https://openalex.org/W2217701558","https://openalex.org/W2317515691","https://openalex.org/W2408246687","https://openalex.org/W2492307518","https://openalex.org/W2560070550","https://openalex.org/W2582743722","https://openalex.org/W2593914038","https://openalex.org/W2883387735","https://openalex.org/W2919821184","https://openalex.org/W2944152053","https://openalex.org/W2979750067","https://openalex.org/W2988589399","https://openalex.org/W3022935508","https://openalex.org/W3039503982","https://openalex.org/W3118913631","https://openalex.org/W4220726338"],"related_works":["https://openalex.org/W2381926679","https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W1549363203","https://openalex.org/W4231274751","https://openalex.org/W2154063878","https://openalex.org/W2556012038","https://openalex.org/W1489772951","https://openalex.org/W3082059448","https://openalex.org/W4313640622"],"abstract_inverted_index":{"The":[0,59],"aim":[1],"of":[2,34,55,76],"this":[3],"article":[4],"is":[5],"to":[6,51,138],"propose":[7,32],"unsupervised":[8],"classification":[9],"methods":[10,21,119],"for":[11,65],"size-and-shape":[12],"considering":[13,128],"two-dimensional":[14],"images":[15],"(planar":[16],"shapes).":[17],"We":[18,30,42],"present":[19],"new":[20],"based":[22],"on":[23,124],"hypothesis":[24],"testing":[25],"and":[26,40,98,112,116,131],"the":[27,53,56,66,70,74,77,121,125,129,134,140],"K-means":[28,115,118,136],"algorithm.":[29],"also":[31],"combinations":[33],"algorithms":[35,78,83],"using":[36],"ensemble":[37],"methods:":[38],"bagging":[39],"boosting.":[41],"consider":[43],"simulated":[44],"data":[45,67],"in":[46,49],"three":[47],"scenarios":[48],"order":[50],"evaluate":[52],"performance":[54,75,123],"proposed":[57],"methods.":[58],"numerical":[60],"results":[61],"have":[62,92,104],"indicated":[63],"that":[64],"sets,":[68],"when":[69],"centroid":[71],"sizes":[72],"change,":[73],"improves.":[79],"In":[80],"addition,":[81],"bagging-based":[82],"outperform":[84],"their":[85],"basic":[86],"versions.":[87],"Moreover,":[88],"two":[89],"real-world":[90],"datasets":[91,103],"been":[93],"considered:":[94],"great":[95],"ape":[96],"skull":[97],"mice":[99],"vertebrae":[100],"references.":[101],"These":[102],"different":[105],"configurations,":[106],"such":[107],"as":[108],"multiple":[109],"reference":[110],"points":[111],"variability.":[113],"Bagged":[114],"boosted":[117],"achieved":[120],"best":[122,141],"datasets.":[126],"Lastly,":[127],"synthetic":[130],"real":[132],"data,":[133],"bagged":[135],"proved":[137],"be":[139],"method.":[142]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}