{"id":"https://openalex.org/W2742048379","doi":"https://doi.org/10.24963/ijcai.2017/330","title":"Deep Ordinal Regression Based on Data Relationship for Small Datasets","display_name":"Deep Ordinal Regression Based on Data Relationship for Small Datasets","publication_year":2017,"publication_date":"2017-07-28","ids":{"openalex":"https://openalex.org/W2742048379","doi":"https://doi.org/10.24963/ijcai.2017/330","mag":"2742048379"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2017/330","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2017/330","pdf_url":"https://www.ijcai.org/proceedings/2017/0330.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2017/0330.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012901717","display_name":"Yanzhu Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yanzhu Liu","raw_affiliation_strings":["Nanyang Technological University","Rolls-Royce Advanced Technology Centre, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanyang Technological University","institution_ids":["https://openalex.org/I172675005"]},{"raw_affiliation_string":"Rolls-Royce Advanced Technology Centre, Singapore","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047970310","display_name":"Adams Wai\u2010Kin Kong","orcid":"https://orcid.org/0000-0002-9728-9511"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Adams Wai Kin Kong","raw_affiliation_strings":["Nanyang Technological University","Rolls-Royce Advanced Technology Centre, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanyang Technological University","institution_ids":["https://openalex.org/I172675005"]},{"raw_affiliation_string":"Rolls-Royce Advanced Technology Centre, Singapore","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021045741","display_name":"Chi-Keong Goh","orcid":"https://orcid.org/0000-0002-4250-7307"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chi Keong Goh","raw_affiliation_strings":["Rolls-Royce Advanced Technology Centre, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rolls-Royce Advanced Technology Centre, Singapore","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":33,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2372","last_page":"2378"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9973000288009644,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9973000288009644,"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":0.9970999956130981,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9921000003814697,"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/ordinal-regression","display_name":"Ordinal regression","score":0.8121755123138428},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6926273703575134},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6856921911239624},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5582482218742371},{"id":"https://openalex.org/keywords/ordinal-data","display_name":"Ordinal data","score":0.548408031463623},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.530509889125824},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5102128982543945},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.474514901638031},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.44174474477767944},{"id":"https://openalex.org/keywords/permutation","display_name":"Permutation (music)","score":0.4323943257331848},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4155435264110565},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.344321608543396},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16958239674568176},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14556801319122314}],"concepts":[{"id":"https://openalex.org/C110313322","wikidata":"https://www.wikidata.org/wiki/Q7100793","display_name":"Ordinal regression","level":2,"score":0.8121755123138428},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6926273703575134},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6856921911239624},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5582482218742371},{"id":"https://openalex.org/C85461838","wikidata":"https://www.wikidata.org/wiki/Q7100785","display_name":"Ordinal data","level":2,"score":0.548408031463623},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.530509889125824},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5102128982543945},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.474514901638031},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.44174474477767944},{"id":"https://openalex.org/C21308566","wikidata":"https://www.wikidata.org/wiki/Q7169365","display_name":"Permutation (music)","level":2,"score":0.4323943257331848},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4155435264110565},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.344321608543396},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16958239674568176},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14556801319122314},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2017/330","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2017/330","pdf_url":"https://www.ijcai.org/proceedings/2017/0330.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2017/330","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2017/330","pdf_url":"https://www.ijcai.org/proceedings/2017/0330.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2742048379.pdf","grobid_xml":"https://content.openalex.org/works/W2742048379.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W29351105","https://openalex.org/W114375244","https://openalex.org/W174507466","https://openalex.org/W573643533","https://openalex.org/W978732560","https://openalex.org/W1980896222","https://openalex.org/W1988254092","https://openalex.org/W1997855593","https://openalex.org/W2112796928","https://openalex.org/W2124648328","https://openalex.org/W2131490903","https://openalex.org/W2142575165","https://openalex.org/W2163605009","https://openalex.org/W2190044943","https://openalex.org/W2295052165","https://openalex.org/W2440214111","https://openalex.org/W2988119488","https://openalex.org/W3011144947","https://openalex.org/W4298353238"],"related_works":["https://openalex.org/W4399574212","https://openalex.org/W2798701209","https://openalex.org/W4300104397","https://openalex.org/W2912776266","https://openalex.org/W4399569456","https://openalex.org/W2503289023","https://openalex.org/W4236496007","https://openalex.org/W1539030525","https://openalex.org/W2313068166","https://openalex.org/W1572610764"],"abstract_inverted_index":{"Ordinal":[0],"regression":[1,37,117],"aims":[2],"to":[3,39,51,89,160],"classify":[4],"instances":[5,47,81],"into":[6],"ordinal":[7,19,36,62,116],"categories.":[8],"As":[9],"with":[10,46,82,149],"other":[11,80,150],"supervised":[12],"learning":[13,15,112,144],"problems,":[14],"an":[16],"effective":[17,162],"deep":[18,53,111,143],"model":[20],"from":[21,48],"a":[22,30,76],"small":[23,120],"dataset":[24],"is":[25,87,147],"challenging.":[26],"This":[27],"paper":[28],"proposes":[29],"new":[31],"approach":[32,145],"which":[33,153],"transforms":[34],"the":[35,69,91,94,99,102,106,125,137,141],"problem":[38],"binary":[40],"classification":[41],"problems":[42],"and":[43,79,130,146],"uses":[44],"triplets":[45,72],"different":[49],"categories":[50],"train":[52],"neural":[54],"networks":[55],"such":[56],"that":[57,136],"high-level":[58],"features":[59],"describing":[60],"their":[61],"relationship":[63],"can":[64,113],"be":[65],"extracted":[66],"automatically.":[67],"In":[68],"testing":[70,77,95],"phase,":[71],"are":[73,154],"formed":[74],"by":[75,109],"instance":[78,96],"known":[83],"ranks.":[84],"A":[85],"decoder":[86],"designed":[88],"estimate":[90],"rank":[92],"of":[93,101,105],"based":[97,156],"on":[98,119,124,157],"outputs":[100],"network.":[103],"Because":[104],"data":[107],"argumentation":[108],"permutation,":[110],"work":[114],"for":[115],"even":[118],"datasets.":[121],"Experimental":[122],"results":[123],"historical":[126],"color":[127],"image":[128,132],"benchmark":[129],"MSRA":[131],"search":[133],"datasets":[134],"demonstrate":[135],"proposed":[138],"algorithm":[139],"outperforms":[140],"traditional":[142],"comparable":[148],"state-of-the-art":[151],"methods,":[152],"highly":[155],"prior":[158],"knowledge":[159],"design":[161],"features.":[163]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
