{"id":"https://openalex.org/W2162895847","doi":"https://doi.org/10.1109/fskd.2012.6234152","title":"Multi-partite ranking with multi-class AdaBoost algorithm","display_name":"Multi-partite ranking with multi-class AdaBoost algorithm","publication_year":2012,"publication_date":"2012-05-01","ids":{"openalex":"https://openalex.org/W2162895847","doi":"https://doi.org/10.1109/fskd.2012.6234152","mag":"2162895847"},"language":"en","primary_location":{"id":"doi:10.1109/fskd.2012.6234152","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2012.6234152","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 9th International Conference on Fuzzy Systems and Knowledge Discovery","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/A5011077051","display_name":"Xiaobo Jin","orcid":"https://orcid.org/0000-0003-1671-1379"},"institutions":[{"id":"https://openalex.org/I36152291","display_name":"Henan University of Technology","ror":"https://ror.org/05sbgwt55","country_code":"CN","type":"education","lineage":["https://openalex.org/I36152291"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiao-Bo Jin","raw_affiliation_strings":["Henan University of Technology, Henan, China"],"affiliations":[{"raw_affiliation_string":"Henan University of Technology, Henan, China","institution_ids":["https://openalex.org/I36152291"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064951946","display_name":"Junwei Yu","orcid":"https://orcid.org/0000-0003-2737-9789"},"institutions":[{"id":"https://openalex.org/I36152291","display_name":"Henan University of Technology","ror":"https://ror.org/05sbgwt55","country_code":"CN","type":"education","lineage":["https://openalex.org/I36152291"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junwei Yu","raw_affiliation_strings":["Henan University of Technology, Henan, China"],"affiliations":[{"raw_affiliation_string":"Henan University of Technology, Henan, China","institution_ids":["https://openalex.org/I36152291"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040628272","display_name":"Dexian Zhang","orcid":"https://orcid.org/0000-0002-9958-0791"},"institutions":[{"id":"https://openalex.org/I36152291","display_name":"Henan University of Technology","ror":"https://ror.org/05sbgwt55","country_code":"CN","type":"education","lineage":["https://openalex.org/I36152291"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dexian Zhang","raw_affiliation_strings":["Henan University of Technology, Henan, China"],"affiliations":[{"raw_affiliation_string":"Henan University of Technology, Henan, China","institution_ids":["https://openalex.org/I36152291"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035643851","display_name":"Guanggang Geng","orcid":"https://orcid.org/0000-0002-9039-6883"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guang-Gang Geng","raw_affiliation_strings":["Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5011077051"],"corresponding_institution_ids":["https://openalex.org/I36152291"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.18200612,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"884","last_page":"887"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9950000047683716,"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/T10057","display_name":"Face and Expression Recognition","score":0.9950000047683716,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9939000010490417,"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/T11550","display_name":"Text and Document Classification Technologies","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/adaboost","display_name":"AdaBoost","score":0.7401288747787476},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.7291464805603027},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.720387876033783},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.6947605609893799},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6441649794578552},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5888593196868896},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5755353569984436},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.47505685687065125},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.46741509437561035},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.45104503631591797},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4235430359840393},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.42282602190971375},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3630366921424866},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3327064514160156},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2344076931476593},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13831281661987305},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.10757717490196228}],"concepts":[{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.7401288747787476},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.7291464805603027},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.720387876033783},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6947605609893799},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6441649794578552},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5888593196868896},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5755353569984436},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.47505685687065125},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.46741509437561035},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.45104503631591797},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4235430359840393},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42282602190971375},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3630366921424866},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3327064514160156},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2344076931476593},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13831281661987305},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.10757717490196228},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fskd.2012.6234152","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2012.6234152","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 9th International Conference on Fuzzy Systems and Knowledge Discovery","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.800000011920929,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W85972746","https://openalex.org/W1552767446","https://openalex.org/W1663973292","https://openalex.org/W1765545080","https://openalex.org/W1841840321","https://openalex.org/W2032210760","https://openalex.org/W2047221353","https://openalex.org/W2053463056","https://openalex.org/W2120391124","https://openalex.org/W2128877075","https://openalex.org/W2155211665","https://openalex.org/W2171541062","https://openalex.org/W2784591815","https://openalex.org/W3175417087","https://openalex.org/W4244952642","https://openalex.org/W4285719527","https://openalex.org/W6603511624","https://openalex.org/W6607690188","https://openalex.org/W6632992187","https://openalex.org/W6637995851","https://openalex.org/W6677732584","https://openalex.org/W6682975109","https://openalex.org/W6685221346","https://openalex.org/W6748062495"],"related_works":["https://openalex.org/W3127142483","https://openalex.org/W4385565564","https://openalex.org/W2898073868","https://openalex.org/W2138488530","https://openalex.org/W4390446658","https://openalex.org/W4387977367","https://openalex.org/W2971071571","https://openalex.org/W2141272333","https://openalex.org/W2798835721","https://openalex.org/W2922169395"],"abstract_inverted_index":{"The":[0],"algorithms":[1,44],"on":[2,23],"learning":[3],"to":[4,33],"rank":[5],"can":[6,72],"traditionally":[7],"be":[8,73],"categorized":[9],"as":[10,102],"three":[11],"classes":[12],"including":[13],"point-wise,":[14],"pair-wise":[15,123],"and":[16,49,130],"list-wise.":[17],"In":[18,111],"our":[19,116],"work,":[20],"we":[21],"focus":[22],"the":[24,28,34,37,46,50,55,58,61,64,76,79,82,91,96,99,103,108,112,122,127,137,145],"regression-based":[25],"method":[26,94,124],"for":[27],"multi-partite":[29],"ranking":[30,43],"problems":[31],"due":[32],"efficiency":[35],"of":[36,57,63,78,107,132],"point-wise":[38],"methods.":[39],"We":[40,68],"proposed":[41],"two":[42],"with":[45,60,81],"real":[47],"AdaBoost":[48],"discrete":[51],"AdaBoost,":[52],"which":[53],"compute":[54],"expectation":[56],"ratings":[59],"estimation":[62],"pseudo":[65],"posterior":[66],"probabilities.":[67],"found":[69],"that":[70],"it":[71],"explained":[74],"in":[75],"framework":[77],"regression":[80,109],"squared":[83],"loss.":[84],"It":[85,135],"is":[86],"more":[87],"easily":[88],"implemented":[89],"than":[90,121,142],"previous":[92],"McRank":[93],"since":[95],"algorithm":[97],"adopts":[98],"decision":[100],"stump":[101],"weak":[104],"leaner":[105],"instead":[106],"tree.":[110],"fifteen":[113],"benchmark":[114],"datasets,":[115],"methods":[117],"achieve":[118],"better":[119],"performance":[120],"RankBoost":[125,143],"under":[126],"C-index,":[128],"NDCG":[129,133],"variant":[131],"measures.":[134],"has":[136],"lower":[138],"training":[139],"time":[140,148],"complexity":[141],"but":[144],"identical":[146],"test":[147],"complexity.":[149]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
