{"id":"https://openalex.org/W2907071956","doi":"https://doi.org/10.1109/icsai.2018.8599448","title":"How Does the Data set Affect CNN-based Image Classification Performance?","display_name":"How Does the Data set Affect CNN-based Image Classification Performance?","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2907071956","doi":"https://doi.org/10.1109/icsai.2018.8599448","mag":"2907071956"},"language":"en","primary_location":{"id":"doi:10.1109/icsai.2018.8599448","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsai.2018.8599448","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 5th International Conference on Systems and Informatics (ICSAI)","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/A5101720733","display_name":"Chao Luo","orcid":"https://orcid.org/0000-0002-5329-946X"},"institutions":[{"id":"https://openalex.org/I24201400","display_name":"Chengdu University of Information Technology","ror":"https://ror.org/01yxwrh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I24201400"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chao Luo","raw_affiliation_strings":["Chengdu University of Information Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Chengdu University of Information Technology, Chengdu, China","institution_ids":["https://openalex.org/I24201400"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100428843","display_name":"Xiaojie Li","orcid":"https://orcid.org/0000-0001-6449-2727"},"institutions":[{"id":"https://openalex.org/I24201400","display_name":"Chengdu University of Information Technology","ror":"https://ror.org/01yxwrh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I24201400"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaojie Li","raw_affiliation_strings":["Chengdu University of Information Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Chengdu University of Information Technology, Chengdu, China","institution_ids":["https://openalex.org/I24201400"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030547287","display_name":"Lutao Wang","orcid":"https://orcid.org/0000-0002-0743-7104"},"institutions":[{"id":"https://openalex.org/I24201400","display_name":"Chengdu University of Information Technology","ror":"https://ror.org/01yxwrh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I24201400"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lutao Wang","raw_affiliation_strings":["Chengdu University of Information Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Chengdu University of Information Technology, Chengdu, China","institution_ids":["https://openalex.org/I24201400"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100601604","display_name":"Jia He","orcid":"https://orcid.org/0000-0002-3508-9477"},"institutions":[{"id":"https://openalex.org/I24201400","display_name":"Chengdu University of Information Technology","ror":"https://ror.org/01yxwrh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I24201400"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia He","raw_affiliation_strings":["Chengdu University of Information Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Chengdu University of Information Technology, Chengdu, China","institution_ids":["https://openalex.org/I24201400"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034114026","display_name":"Denggao Li","orcid":null},"institutions":[{"id":"https://openalex.org/I24201400","display_name":"Chengdu University of Information Technology","ror":"https://ror.org/01yxwrh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I24201400"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Denggao Li","raw_affiliation_strings":["Chengdu University of Information Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Chengdu University of Information Technology, Chengdu, China","institution_ids":["https://openalex.org/I24201400"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110286429","display_name":"Jiliu Zhou","orcid":"https://orcid.org/0009-0002-0221-905X"},"institutions":[{"id":"https://openalex.org/I24201400","display_name":"Chengdu University of Information Technology","ror":"https://ror.org/01yxwrh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I24201400"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiliu Zhou","raw_affiliation_strings":["Chengdu University of Information Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Chengdu University of Information Technology, Chengdu, China","institution_ids":["https://openalex.org/I24201400"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101720733"],"corresponding_institution_ids":["https://openalex.org/I24201400"],"apc_list":null,"apc_paid":null,"fwci":1.5669,"has_fulltext":false,"cited_by_count":89,"citation_normalized_percentile":{"value":0.88064799,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"361","last_page":"366"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9994999766349792,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9987000226974487,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.758010983467102},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6998291015625},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.651382327079773},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6478519439697266},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.614691972732544},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.6060046553611755},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.573685884475708},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5620478391647339},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5537939667701721},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.5103134512901306},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39550578594207764}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.758010983467102},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6998291015625},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.651382327079773},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6478519439697266},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.614691972732544},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.6060046553611755},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.573685884475708},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5620478391647339},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5537939667701721},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.5103134512901306},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39550578594207764},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icsai.2018.8599448","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsai.2018.8599448","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 5th International Conference on Systems and Informatics (ICSAI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1486469731","https://openalex.org/W1677182931","https://openalex.org/W1967400946","https://openalex.org/W2046103274","https://openalex.org/W2046468425","https://openalex.org/W2063217105","https://openalex.org/W2111457156","https://openalex.org/W2117539524","https://openalex.org/W2128154306","https://openalex.org/W2153771889","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2202310977","https://openalex.org/W2216949779","https://openalex.org/W2263295684","https://openalex.org/W2295628041","https://openalex.org/W2302255633","https://openalex.org/W2319888919","https://openalex.org/W2359237450","https://openalex.org/W2390898427","https://openalex.org/W2402669826","https://openalex.org/W2531634031","https://openalex.org/W2551161082","https://openalex.org/W2584555536","https://openalex.org/W2617297862","https://openalex.org/W2789621689","https://openalex.org/W2794026873","https://openalex.org/W2794865285","https://openalex.org/W2795227883","https://openalex.org/W2949455921","https://openalex.org/W2963745697","https://openalex.org/W2964227963","https://openalex.org/W3009217644","https://openalex.org/W6637342671","https://openalex.org/W6684191040","https://openalex.org/W6688003611","https://openalex.org/W6688365552","https://openalex.org/W6698183232","https://openalex.org/W6732978085","https://openalex.org/W6749962334","https://openalex.org/W7071075734"],"related_works":["https://openalex.org/W2911497689","https://openalex.org/W2952813363","https://openalex.org/W2963346891","https://openalex.org/W4360783045","https://openalex.org/W2770149305","https://openalex.org/W3167930666","https://openalex.org/W3185156046","https://openalex.org/W3014952856","https://openalex.org/W3010730661","https://openalex.org/W1990237101"],"abstract_inverted_index":{"Convolutional":[0],"neural":[1],"networks":[2],"(ConvNets":[3],"or":[4],"CNNs)":[5],"have":[6,83,243],"been":[7],"proven":[8],"very":[9],"effective":[10],"in":[11,20,137,142,162,210],"areas":[12],"such":[13],"as":[14],"image":[15,24,61,239],"recognition":[16],"and":[17,71],"classification.":[18],"Especially":[19],"the":[21,26,67,72,75,113,116,154,157,166,175,186,188,191,193,198,214,220,223],"field":[22],"of":[23,74,96,108,115,156,168,181,227],"classification,":[25,240],"CNN-based":[27,42,177],"method":[28,44],"has":[29],"achieved":[30],"excellent":[31],"performance.":[32,56,88],"Performance":[33],"is":[34,45,49,64],"an":[35],"important":[36,50,244],"indicator":[37],"for":[38,90,247],"evaluating":[39],"whether":[40],"a":[41,84,93,106],"classification":[43,62],"excellent,":[46],"so":[47],"it":[48],"to":[51,101,123,164,235],"study":[52],"which":[53],"factors":[54],"affect":[55,119],"As":[57],"we":[58,104,148],"all":[59],"know,":[60],"performance":[63,224],"affected":[65],"by":[66,152],"network":[68],"structure":[69],"itself":[70],"size":[73,82,114,155,171],"data":[76,80,97,117,169,200],"set.":[77,159],"In":[78,121],"particular,":[79],"set":[81,98,118,170,201],"significant":[85],"impact":[86],"on":[87,172,238,251],"While":[89],"most":[91],"people,":[92],"large":[94],"number":[95],"are":[99,128,202,218],"difficult":[100],"obtain.":[102],"Thus,":[103],"consider":[105],"question":[107],"this":[109,125,228],"approach:":[110],"How":[111],"does":[112],"performance?":[120],"order":[122],"clarify":[124],"issue,":[126],"there":[127],"35":[129],"groups":[130,151],"experiment":[131,136],"performed":[132],"with":[133],"5":[134,150],"times":[135],"each":[138,145,211],"group":[139,212],"(175":[140],"experiments":[141,237,249],"total).":[143],"For":[144,174],"k-classification":[146],"experiment,":[147,213],"do":[149,236],"increasing":[153],"training":[158,189,199],"Observe":[160],"changes":[161],"accuracy":[163,183],"analyze":[165],"effect":[167],"difference.":[173],"same":[176],"network,":[178],"experimental":[179],"results":[180,205],"average":[182],"illustrate":[184],"that":[185,217],"larger":[187],"set,":[190],"higher":[192],"test":[194],"accuracy.":[195],"However,":[196],"when":[197],"insufficient,":[203],"better":[204],"can":[206,232],"be":[207],"obtained.":[208],"Furthermore,":[209],"more":[215,221],"categories":[216],"classified,":[219],"obvious":[222],"change.":[225],"Results":[226],"paper":[229],"not":[230],"only":[231],"guide":[233],"us":[234],"but":[241],"also":[242],"guiding":[245],"significance":[246],"other":[248],"based":[250],"deep":[252],"learning.":[253]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":19},{"year":2022,"cited_by_count":13},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}