{"id":"https://openalex.org/W2931817899","doi":"https://doi.org/10.1109/bigcomp.2019.8678924","title":"Evaluation of Stratified Validation in Neural Network Training with Imbalanced Data","display_name":"Evaluation of Stratified Validation in Neural Network Training with Imbalanced Data","publication_year":2019,"publication_date":"2019-02-01","ids":{"openalex":"https://openalex.org/W2931817899","doi":"https://doi.org/10.1109/bigcomp.2019.8678924","mag":"2931817899"},"language":"en","primary_location":{"id":"doi:10.1109/bigcomp.2019.8678924","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigcomp.2019.8678924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data and Smart Computing (BigComp)","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/A5024516449","display_name":"Tomoumi Takase","orcid":"https://orcid.org/0000-0001-6974-8103"},"institutions":[{"id":"https://openalex.org/I73613424","display_name":"National Institute of Advanced Industrial Science and Technology","ror":"https://ror.org/01703db54","country_code":"JP","type":"government","lineage":["https://openalex.org/I73613424"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomoumi Takase","raw_affiliation_strings":["Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan","institution_ids":["https://openalex.org/I73613424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056709028","display_name":"Satoshi Oyama","orcid":"https://orcid.org/0000-0002-8124-3578"},"institutions":[{"id":"https://openalex.org/I205349734","display_name":"Hokkaido University","ror":"https://ror.org/02e16g702","country_code":"JP","type":"education","lineage":["https://openalex.org/I205349734"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Satoshi Oyama","raw_affiliation_strings":["Global Institution for Collaborative Research and Education, Hokkaido University, Sapporo, Hokkaido, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Global Institution for Collaborative Research and Education, Hokkaido University, Sapporo, Hokkaido, Japan","institution_ids":["https://openalex.org/I205349734"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054412358","display_name":"Masahito Kurihara","orcid":"https://orcid.org/0000-0002-1478-1093"},"institutions":[{"id":"https://openalex.org/I205349734","display_name":"Hokkaido University","ror":"https://ror.org/02e16g702","country_code":"JP","type":"education","lineage":["https://openalex.org/I205349734"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masahito Kurihara","raw_affiliation_strings":["Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Hokkaido, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Hokkaido, Japan","institution_ids":["https://openalex.org/I205349734"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5784,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.74865041,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9983999729156494,"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":0.9983999729156494,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9959999918937683,"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/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/computer-science","display_name":"Computer science","score":0.7548457980155945},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6833850145339966},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6654148101806641},{"id":"https://openalex.org/keywords/cross-validation","display_name":"Cross-validation","score":0.6464129090309143},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5901692509651184},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5867411494255066},{"id":"https://openalex.org/keywords/data-validation","display_name":"Data validation","score":0.5551450848579407},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.550197184085846},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.5492909550666809},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5157992839813232},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5118926763534546},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5114063620567322},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.41288474202156067},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0978679358959198}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7548457980155945},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6833850145339966},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6654148101806641},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.6464129090309143},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5901692509651184},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5867411494255066},{"id":"https://openalex.org/C92446256","wikidata":"https://www.wikidata.org/wiki/Q3306762","display_name":"Data validation","level":2,"score":0.5551450848579407},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.550197184085846},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.5492909550666809},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5157992839813232},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5118926763534546},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5114063620567322},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.41288474202156067},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0978679358959198},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"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/bigcomp.2019.8678924","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigcomp.2019.8678924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data and Smart Computing (BigComp)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320336288","display_name":"Global Institution for Collaborative Research and Education, Hokkaido University","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1533861849","https://openalex.org/W1836465849","https://openalex.org/W1965555277","https://openalex.org/W2001619934","https://openalex.org/W2008973833","https://openalex.org/W2095705004","https://openalex.org/W2112796928","https://openalex.org/W2156387975","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2330820318","https://openalex.org/W2335728318","https://openalex.org/W2914484425","https://openalex.org/W3085162807","https://openalex.org/W3118608800","https://openalex.org/W4238404964","https://openalex.org/W6631943919","https://openalex.org/W6638667902","https://openalex.org/W6674330103","https://openalex.org/W6682889407","https://openalex.org/W6684191040","https://openalex.org/W6703116779"],"related_works":["https://openalex.org/W2076543106","https://openalex.org/W2523437662","https://openalex.org/W89844371","https://openalex.org/W2019891950","https://openalex.org/W2085842814","https://openalex.org/W4286643620","https://openalex.org/W4387048144","https://openalex.org/W2492135063","https://openalex.org/W2362514456","https://openalex.org/W2136232598"],"abstract_inverted_index":{"Validation":[0],"data":[1,32,63],"is":[2,24,55],"widely":[3],"used":[4,50],"for":[5,41,59,71,92],"measuring":[6],"the":[7,66,69,75,78,83,89],"generalization":[8],"performance":[9],"of":[10,68,74,82],"models":[11],"in":[12,37],"machine":[13],"learning":[14],"such":[15],"as":[16,80],"neural":[17,98],"network":[18],"training.":[19],"However,":[20],"because":[21],"usually":[22],"it":[23],"randomly":[25],"extracted":[26],"from":[27],"training":[28,84],"data,":[29],"imbalanced":[30],"validation":[31,40,62,76,91],"can":[33],"be":[34],"obtained,":[35],"resulting":[36],"an":[38],"unstable":[39],"models.":[42],"To":[43],"deal":[44],"with":[45,100],"this":[46],"problem,":[47],"we":[48],"have":[49,87],"a":[51,56,93],"stratified":[52,90],"validation,":[53],"which":[54],"conventional":[57],"method":[58],"preparing":[60],"balanced":[61],"by":[64],"making":[65],"ratio":[67],"numbers":[70],"each":[72],"class":[73],"samples":[77],"same":[79],"that":[81],"samples.":[85],"We":[86],"evaluated":[88],"multilayer":[94],"perceptron":[95],"and":[96],"convolutional":[97],"networks":[99],"several":[101],"benchmark":[102],"datasets.":[103]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}