{"id":"https://openalex.org/W4212936505","doi":"https://doi.org/10.1109/tai.2022.3151724","title":"A Hybrid Method to Measure Distribution Consistency of Mixed-Attribute Datasets","display_name":"A Hybrid Method to Measure Distribution Consistency of Mixed-Attribute Datasets","publication_year":2022,"publication_date":"2022-02-16","ids":{"openalex":"https://openalex.org/W4212936505","doi":"https://doi.org/10.1109/tai.2022.3151724"},"language":"en","primary_location":{"id":"doi:10.1109/tai.2022.3151724","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tai.2022.3151724","pdf_url":null,"source":{"id":"https://openalex.org/S4210169448","display_name":"IEEE Transactions on Artificial Intelligence","issn_l":"2691-4581","issn":["2691-4581"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Artificial Intelligence","raw_type":"journal-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/A5076542236","display_name":"Yulin He","orcid":"https://orcid.org/0000-0002-3415-0686"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yulin He","raw_affiliation_strings":["College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China","National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]},{"raw_affiliation_string":"National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100644365","display_name":"Xuan Ye","orcid":"https://orcid.org/0000-0002-5509-0854"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuan Ye","raw_affiliation_strings":["College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China","National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]},{"raw_affiliation_string":"National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046144654","display_name":"Defa Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Defa Huang","raw_affiliation_strings":["College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China","National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]},{"raw_affiliation_string":"National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038682039","display_name":"Philippe Fournier\u2010Viger","orcid":"https://orcid.org/0000-0002-7680-9899"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Philippe Fournier-Viger","raw_affiliation_strings":["College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China","National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]},{"raw_affiliation_string":"National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003347359","display_name":"Joshua Zhexue Huang","orcid":"https://orcid.org/0000-0002-6797-2571"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Joshua Zhexue Huang","raw_affiliation_strings":["College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China","National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]},{"raw_affiliation_string":"National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5076542236"],"corresponding_institution_ids":["https://openalex.org/I180726961"],"apc_list":null,"apc_paid":null,"fwci":0.6631,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.72829789,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"4","issue":"1","first_page":"182","last_page":"196"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":1.0,"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/T12676","display_name":"Machine Learning and ELM","score":1.0,"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":0.9869999885559082,"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/T10057","display_name":"Face and Expression Recognition","score":0.9829000234603882,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.7398578524589539},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6715513467788696},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.6682048439979553},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6082025766372681},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.538192093372345},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.5269395112991333},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.4917600452899933},{"id":"https://openalex.org/keywords/data-consistency","display_name":"Data consistency","score":0.44339799880981445},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35662561655044556},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3154720962047577},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3098936975002289},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.07291239500045776}],"concepts":[{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.7398578524589539},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6715513467788696},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.6682048439979553},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6082025766372681},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.538192093372345},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.5269395112991333},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4917600452899933},{"id":"https://openalex.org/C93361087","wikidata":"https://www.wikidata.org/wiki/Q4426698","display_name":"Data consistency","level":2,"score":0.44339799880981445},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35662561655044556},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3154720962047577},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3098936975002289},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.07291239500045776},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tai.2022.3151724","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tai.2022.3151724","pdf_url":null,"source":{"id":"https://openalex.org/S4210169448","display_name":"IEEE Transactions on Artificial Intelligence","issn_l":"2691-4581","issn":["2691-4581"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1202743199","https://openalex.org/W1491541693","https://openalex.org/W1503398984","https://openalex.org/W1505837402","https://openalex.org/W1974098896","https://openalex.org/W2026131661","https://openalex.org/W2045979066","https://openalex.org/W2111072639","https://openalex.org/W2118020555","https://openalex.org/W2119655341","https://openalex.org/W2141695047","https://openalex.org/W2301541953","https://openalex.org/W2516004608","https://openalex.org/W2756439134","https://openalex.org/W2773612851","https://openalex.org/W2808726866","https://openalex.org/W2906300927","https://openalex.org/W2948194767","https://openalex.org/W2948646149","https://openalex.org/W2989661724","https://openalex.org/W3018761215","https://openalex.org/W3200768790","https://openalex.org/W4240360304","https://openalex.org/W6688325169"],"related_works":["https://openalex.org/W2386805970","https://openalex.org/W2308026613","https://openalex.org/W2379416403","https://openalex.org/W2901600492","https://openalex.org/W2165253724","https://openalex.org/W1985571776","https://openalex.org/W2769374332","https://openalex.org/W2971748376","https://openalex.org/W2167069786","https://openalex.org/W2749515417"],"abstract_inverted_index":{"Random":[0],"sample":[1],"partition":[2],"(RSP)":[3],"is":[4,30,92,109],"a":[5,60,74],"newly":[6],"developed":[7],"data":[8,146,164],"management":[9],"and":[10,17,46,80,130,162,166],"processing":[11,16],"model":[12,23],"for":[13,24,44],"Big":[14,25],"Data":[15,26],"analysis.":[18],"To":[19],"apply":[20],"the":[21,35,65,81,106,113,117,128,135,140,148,157,167,177,183],"RSP":[22,161],"computation":[27],"tasks,":[28],"it":[29],"very":[31],"important":[32],"to":[33,63,95,111,126],"measure":[34,64,182],"distribution":[36,66,114,158,184],"consistency":[37,67,115,185],"of":[38,116,132,137,142,150,154,160,186],"different":[39,69],"datasets.":[40,54,104,120],"Existing":[41],"measurement":[42,171],"methods":[43],"continuous-attribute":[45],"discrete-attribute":[47],"datasets":[48,71,99,188],"cannot":[49],"directly":[50],"deal":[51],"with":[52,169],"mixed-attribute":[53,70,98,145,155,187],"In":[55],"this":[56],"article,":[57],"we":[58],"design":[59],"hybrid":[61],"method":[62,180],"among":[68],"by":[72],"using":[73],"multilayer":[75],"extreme":[76],"learning":[77],"machine":[78],"(MLELM)":[79],"generalized":[82],"maximum":[83],"mean":[84],"discrepancy":[85],"(GMMD)":[86],"criterion,":[87],"abbreviated":[88],"as":[89],"MLELM-GMMD.":[90],"MLELM":[91,138,151],"first":[93],"used":[94],"transform":[96],"original":[97],"into":[100],"corresponding":[101],"deep":[102,118],"encoding":[103,119],"Then,":[105],"GMMD":[107],"criterion":[108],"applied":[110],"check":[112],"Four":[121],"experiments":[122],"have":[123],"been":[124],"done":[125],"validate":[127],"feasibility":[129],"effectiveness":[131],"MLELM-GMMD,":[133],"i.e.,":[134],"impact":[136,149],"on":[139,152],"amount":[141],"information":[143],"during":[144],"transformation,":[147],"distributions":[153],"data,":[156],"consistencies":[159],"non-RSP":[163],"blocks,":[165],"comparison":[168],"other":[170],"methods.":[172,194],"Experimental":[173],"results":[174],"show":[175],"that":[176],"proposed":[178],"MLELM-GMMD":[179],"can":[181],"more":[189],"accurately":[190],"than":[191],"one-hot":[192],"encoding-based":[193]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}