{"id":"https://openalex.org/W4406611993","doi":"https://doi.org/10.1109/smc54092.2024.10831391","title":"Transforming GP-CNN Tree Search Into Trainable Architectures for Image Classification","display_name":"Transforming GP-CNN Tree Search Into Trainable Architectures for Image Classification","publication_year":2024,"publication_date":"2024-10-06","ids":{"openalex":"https://openalex.org/W4406611993","doi":"https://doi.org/10.1109/smc54092.2024.10831391"},"language":"en","primary_location":{"id":"doi:10.1109/smc54092.2024.10831391","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc54092.2024.10831391","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5108048515","display_name":"Fengchun Sun","orcid":"https://orcid.org/0000-0001-9524-9367"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng Sun","raw_affiliation_strings":["Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China,Shenzhen,China,518110"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China,Shenzhen,China,518110","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100869715","display_name":"Ke Yan","orcid":"https://orcid.org/0000-0001-6314-0762"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan Ke","raw_affiliation_strings":["Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China,Shenzhen,China,518110"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China,Shenzhen,China,518110","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063438356","display_name":"Yue\u2010Jiao Gong","orcid":"https://orcid.org/0000-0002-5648-1160"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue-Jiao Gong","raw_affiliation_strings":["School of Computer Science and Engineering, South China University of Technology,Guangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, South China University of Technology,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051497691","display_name":"Yun Li","orcid":"https://orcid.org/0000-0002-3205-8464"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yun Li","raw_affiliation_strings":["i4AI Ltd, London WCIN3AX,United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"i4AI Ltd, London WCIN3AX,United Kingdom","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1919","last_page":"1926"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9580000042915344,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9580000042915344,"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/T10320","display_name":"Neural Networks and Applications","score":0.9007999897003174,"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":0.7534862756729126},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.644212007522583},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.602533757686615},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.562606692314148},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5417753458023071},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5086191892623901},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11339506506919861}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7534862756729126},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.644212007522583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.602533757686615},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.562606692314148},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5417753458023071},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5086191892623901},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11339506506919861},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc54092.2024.10831391","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc54092.2024.10831391","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1571203491","https://openalex.org/W2027649639","https://openalex.org/W2069036068","https://openalex.org/W2078651972","https://openalex.org/W2111285038","https://openalex.org/W2115770731","https://openalex.org/W2194775991","https://openalex.org/W2896064450","https://openalex.org/W2919115771","https://openalex.org/W2925665976","https://openalex.org/W2946547492","https://openalex.org/W2969199043","https://openalex.org/W3010290582","https://openalex.org/W3036020790","https://openalex.org/W3114259420","https://openalex.org/W4214577967","https://openalex.org/W4226216634","https://openalex.org/W4246193833","https://openalex.org/W4310748454","https://openalex.org/W4379985233","https://openalex.org/W4389776171"],"related_works":["https://openalex.org/W3188962172","https://openalex.org/W2772917594","https://openalex.org/W4312825515","https://openalex.org/W4306742369","https://openalex.org/W4303457083","https://openalex.org/W2131146434","https://openalex.org/W2951359407","https://openalex.org/W2033914206","https://openalex.org/W2042327336","https://openalex.org/W2546503577"],"abstract_inverted_index":{"Data-efficient":[0],"image":[1,67],"classification":[2,128],"poses":[3],"a":[4,38,61],"challenge":[5],"in":[6,84,99],"achieving":[7],"effectiveness":[8],"with":[9,118],"limited":[10],"data,":[11],"as":[12,37],"evidenced":[13],"by":[14,91],"the":[15,48,52,100,103,109,112,131],"current":[16],"methods":[17,33,122,125],"based":[18],"on":[19,126],"convolutional":[20],"neural":[21,120],"networks":[22],"(CNNs)":[23],"and":[24,42,75,108,123],"genetic":[25,62],"programming":[26,63],"(GP).":[27],"Existing":[28],"works":[29],"employing":[30,92],"these":[31,56],"two":[32],"encounter":[34],"limitations,":[35],"such":[36],"lack":[39],"of":[40,51,96,111],"flexibility":[41],"an":[43],"inability":[44],"to":[45],"effectively":[46],"explore":[47],"latent":[49],"features":[50],"data.":[53],"To":[54],"tackle":[55],"challenges,":[57],"this":[58],"paper":[59],"introduces":[60],"method":[64,79,133],"for":[65],"data-efficient":[66,127],"recognition,":[68],"leveraging":[69],"novel":[70],"function":[71],"sets,":[72,74],"terminal":[73],"program":[76],"structures.":[77],"This":[78],"transforms":[80],"tree-based":[81],"data":[82],"structures":[83,94,114],"GP":[85],"into":[86],"trainable":[87],"CNN":[88],"architectures.":[89],"Further,":[90],"block":[93],"instead":[95],"single":[97],"operations":[98],"search":[101,104,113],"space,":[102],"space":[105],"is":[106],"reduced":[107],"stability":[110],"enhanced.":[115],"Comparative":[116],"experiments":[117],"state-of-the-art":[119],"network":[121],"GP-based":[124],"datasets":[129],"validate":[130],"GP-CNN":[132],"offering":[134],"higher":[135],"performance.":[136]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
