{"id":"https://openalex.org/W2171055840","doi":"https://doi.org/10.1109/ijcnn.2005.1556426","title":"An experimental comparison of semi-supervised ARTMAP architectures, GCS and GNG classifiers","display_name":"An experimental comparison of semi-supervised ARTMAP architectures, GCS and GNG classifiers","publication_year":2006,"publication_date":"2006-01-05","ids":{"openalex":"https://openalex.org/W2171055840","doi":"https://doi.org/10.1109/ijcnn.2005.1556426","mag":"2171055840"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2005.1556426","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2005.1556426","pdf_url":null,"source":{"id":"https://openalex.org/S4363609022","display_name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://stars.library.ucf.edu/scopus2000/3263","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049947112","display_name":"Xuan Le","orcid":"https://orcid.org/0000-0003-3616-3840"},"institutions":[{"id":"https://openalex.org/I106959904","display_name":"Florida Institute of Technology","ror":"https://ror.org/04atsbb87","country_code":"US","type":"education","lineage":["https://openalex.org/I106959904"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Quang Le","raw_affiliation_strings":["Computer Sciences, Florida Institute of Technology, Melbourne, FL, USA"],"affiliations":[{"raw_affiliation_string":"Computer Sciences, Florida Institute of Technology, Melbourne, FL, USA","institution_ids":["https://openalex.org/I106959904"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102864967","display_name":"Georgios C. Anagnostopoulos","orcid":"https://orcid.org/0000-0001-6008-0303"},"institutions":[{"id":"https://openalex.org/I106959904","display_name":"Florida Institute of Technology","ror":"https://ror.org/04atsbb87","country_code":"US","type":"education","lineage":["https://openalex.org/I106959904"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"G.C. Anagnostopoulos","raw_affiliation_strings":["Electrical & Computer Engineering, Florida Institute of Technology, Melbourne, FL, USA"],"affiliations":[{"raw_affiliation_string":"Electrical & Computer Engineering, Florida Institute of Technology, Melbourne, FL, USA","institution_ids":["https://openalex.org/I106959904"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036799400","display_name":"Michael Georgiopoulos","orcid":"https://orcid.org/0009-0002-8840-0123"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"M. Georgiopoulos","raw_affiliation_strings":["Electrical Engineering, University of Central Florida, Orlando, FL, USA"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering, University of Central Florida, Orlando, FL, USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073137237","display_name":"Ken Ports","orcid":null},"institutions":[{"id":"https://openalex.org/I106959904","display_name":"Florida Institute of Technology","ror":"https://ror.org/04atsbb87","country_code":"US","type":"education","lineage":["https://openalex.org/I106959904"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"K. Ports","raw_affiliation_strings":["Electrical & Computer Engineering, Florida Institute of Technology, Melbourne, FL, USA"],"affiliations":[{"raw_affiliation_string":"Electrical & Computer Engineering, Florida Institute of Technology, Melbourne, FL, USA","institution_ids":["https://openalex.org/I106959904"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5049947112"],"corresponding_institution_ids":["https://openalex.org/I106959904"],"apc_list":null,"apc_paid":null,"fwci":1.3682,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.81114983,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"5","issue":null,"first_page":"3121","last_page":"3126"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9995999932289124,"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/T10320","display_name":"Neural Networks and Applications","score":0.9995999932289124,"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/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.9975000023841858,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9840999841690063,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7478163838386536},{"id":"https://openalex.org/keywords/random-subspace-method","display_name":"Random subspace method","score":0.6923913955688477},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5819310545921326},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5818187594413757},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.578062117099762},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5726226568222046},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.542901873588562},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.5008585453033447},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.4494820833206177},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.43156126141548157},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.33899009227752686},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23573702573776245}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7478163838386536},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.6923913955688477},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5819310545921326},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5818187594413757},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.578062117099762},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5726226568222046},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.542901873588562},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.5008585453033447},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.4494820833206177},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.43156126141548157},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.33899009227752686},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23573702573776245},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/ijcnn.2005.1556426","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2005.1556426","pdf_url":null,"source":{"id":"https://openalex.org/S4363609022","display_name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.377.389","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.377.389","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://phoenix.fit.edu/~georgio/pubs/Le_ijcnn_2005.pdf","raw_type":"text"},{"id":"pmh:oai:stars.library.ucf.edu:scopus2000-4262","is_oa":true,"landing_page_url":"https://stars.library.ucf.edu/scopus2000/3263","pdf_url":null,"source":{"id":"https://openalex.org/S4210172555","display_name":"Journal of International Crisis and Risk Communication Research","issn_l":"2576-0017","issn":["2576-0017","2576-0025"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Scopus Export 2000s","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:stars.library.ucf.edu:scopus2000-4262","is_oa":true,"landing_page_url":"https://stars.library.ucf.edu/scopus2000/3263","pdf_url":null,"source":{"id":"https://openalex.org/S4210172555","display_name":"Journal of International Crisis and Risk Communication Research","issn_l":"2576-0017","issn":["2576-0017","2576-0025"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Scopus Export 2000s","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W65738273","https://openalex.org/W134309601","https://openalex.org/W1573503290","https://openalex.org/W1797753341","https://openalex.org/W2010150441","https://openalex.org/W2084812512","https://openalex.org/W2103875979","https://openalex.org/W2130677181","https://openalex.org/W2138754805","https://openalex.org/W2166280719","https://openalex.org/W3121926921","https://openalex.org/W3207021134","https://openalex.org/W3207342693","https://openalex.org/W6680294583"],"related_works":["https://openalex.org/W1964832275","https://openalex.org/W2389865566","https://openalex.org/W2407804800","https://openalex.org/W2388864896","https://openalex.org/W2888937984","https://openalex.org/W2031173026","https://openalex.org/W2117265009","https://openalex.org/W2031651184","https://openalex.org/W2913388591","https://openalex.org/W108062576"],"abstract_inverted_index":{"In":[0],"this":[1],"paper":[2],"we":[3,86],"present":[4,87],"an":[5],"experimental":[6],"comparison":[7,31],"of":[8,36,42],"four":[9],"neural-based":[10],"classifiers,":[11,109],"namely":[12],"growing":[13,17],"cell":[14],"structures":[15],"(GCS),":[16],"neural":[18],"gas":[19],"(GNG),":[20],"semi-supervised":[21,26,96],"fuzzy":[22,56],"ARTMAP":[23,28,92],"(ssFAM)":[24],"and":[25,39,72,107],"ellipsoid":[27],"(ssEAM).":[29],"The":[30],"is":[32,83],"performed":[33],"in":[34,51],"terms":[35],"classification":[37],"accuracy":[38],"structural":[40,113],"complexity":[41],"the":[43,52],"resulting":[44],"classifiers.":[45,74],"Earlier":[46],"studies":[47],"that":[48,55,91],"had":[49],"appeared":[50],"literature":[53],"showed":[54],"ARTMAP,":[57],"which":[58],"utilizes":[59],"fully-supervised":[60],"learning,":[61],"may":[62],"suffer":[63],"from":[64],"poor":[65],"generalization":[66],"performance,":[67],"when":[68,79],"compared":[69],"to":[70,105],"GCS":[71,106],"GNG":[73,108],"This":[75],"phenomenon":[76],"typically":[77],"occurs,":[78],"class":[80],"distribution":[81],"overlap":[82],"significant.":[84],"Here,":[85],"new":[88],"results":[89],"indicating":[90],"classifiers":[93],"equipped":[94],"with":[95,103],"learning":[97],"capabilities":[98],"can":[99],"improve":[100],"their":[101],"performance":[102],"respect":[104],"while":[110],"maintaining":[111],"lower":[112],"complexity.":[114]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}