{"id":"https://openalex.org/W2046572888","doi":"https://doi.org/10.1109/ukci.2014.6930159","title":"Kernel learning method for distance-based classification of categorical data","display_name":"Kernel learning method for distance-based classification of categorical data","publication_year":2014,"publication_date":"2014-09-01","ids":{"openalex":"https://openalex.org/W2046572888","doi":"https://doi.org/10.1109/ukci.2014.6930159","mag":"2046572888"},"language":"en","primary_location":{"id":"doi:10.1109/ukci.2014.6930159","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ukci.2014.6930159","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","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/A5091369584","display_name":"Lifei Chen","orcid":"https://orcid.org/0000-0003-3568-8899"},"institutions":[{"id":"https://openalex.org/I111753288","display_name":"Fujian Normal University","ror":"https://ror.org/020azk594","country_code":"CN","type":"education","lineage":["https://openalex.org/I111753288"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lifei Chen","raw_affiliation_strings":["School of Mathematics and Computer Science, Fujian Normal University, Fujian, China","School of Mathematics and Computer Science, Fujian Normal University, Fujian, 350117, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics and Computer Science, Fujian Normal University, Fujian, China","institution_ids":["https://openalex.org/I111753288"]},{"raw_affiliation_string":"School of Mathematics and Computer Science, Fujian Normal University, Fujian, 350117, China","institution_ids":["https://openalex.org/I111753288"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101637886","display_name":"Gongde Guo","orcid":"https://orcid.org/0000-0002-5118-7261"},"institutions":[{"id":"https://openalex.org/I111753288","display_name":"Fujian Normal University","ror":"https://ror.org/020azk594","country_code":"CN","type":"education","lineage":["https://openalex.org/I111753288"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gongde Guo","raw_affiliation_strings":["School of Mathematics and Computer Science, Fujian Normal University, Fujian, China","School of Mathematics and Computer Science, Fujian Normal University, Fujian, 350117, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics and Computer Science, Fujian Normal University, Fujian, China","institution_ids":["https://openalex.org/I111753288"]},{"raw_affiliation_string":"School of Mathematics and Computer Science, Fujian Normal University, Fujian, 350117, China","institution_ids":["https://openalex.org/I111753288"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065104101","display_name":"Shengrui Wang","orcid":"https://orcid.org/0000-0001-6863-7022"},"institutions":[{"id":"https://openalex.org/I111753288","display_name":"Fujian Normal University","ror":"https://ror.org/020azk594","country_code":"CN","type":"education","lineage":["https://openalex.org/I111753288"]},{"id":"https://openalex.org/I135117807","display_name":"Universit\u00e9 de Sherbrooke","ror":"https://ror.org/00kybxq39","country_code":"CA","type":"education","lineage":["https://openalex.org/I135117807"]}],"countries":["CA","CN"],"is_corresponding":false,"raw_author_name":"Shengrui Wang","raw_affiliation_strings":["School of Mathematics and Computer Science, Fujian Normal University, Fujian, China","Department of Computer Science, University of Sherbrooke, Quebec, J1K 2R1, Canada"],"affiliations":[{"raw_affiliation_string":"School of Mathematics and Computer Science, Fujian Normal University, Fujian, China","institution_ids":["https://openalex.org/I111753288"]},{"raw_affiliation_string":"Department of Computer Science, University of Sherbrooke, Quebec, J1K 2R1, Canada","institution_ids":["https://openalex.org/I135117807"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054609349","display_name":"Xiangzeng Kong","orcid":"https://orcid.org/0000-0001-8492-1349"},"institutions":[{"id":"https://openalex.org/I111753288","display_name":"Fujian Normal University","ror":"https://ror.org/020azk594","country_code":"CN","type":"education","lineage":["https://openalex.org/I111753288"]},{"id":"https://openalex.org/I138801177","display_name":"University of Ulster","ror":"https://ror.org/01yp9g959","country_code":"GB","type":"education","lineage":["https://openalex.org/I138801177"]}],"countries":["CN","GB"],"is_corresponding":false,"raw_author_name":"Xiangzeng Kong","raw_affiliation_strings":["School of Mathematics and Computer Science, Fujian Normal University, Fujian, China","School of Computing and Mathematics, University of Ulster at Jordanstown, Northern Ireland BT37 0QB, UK"],"affiliations":[{"raw_affiliation_string":"School of Mathematics and Computer Science, Fujian Normal University, Fujian, China","institution_ids":["https://openalex.org/I111753288"]},{"raw_affiliation_string":"School of Computing and Mathematics, University of Ulster at Jordanstown, Northern Ireland BT37 0QB, UK","institution_ids":["https://openalex.org/I138801177"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5091369584"],"corresponding_institution_ids":["https://openalex.org/I111753288"],"apc_list":null,"apc_paid":null,"fwci":0.48228309,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.70080858,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"10","issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9994000196456909,"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":0.9994000196456909,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9936000108718872,"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":0.993399977684021,"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/categorical-variable","display_name":"Categorical variable","score":0.8404484391212463},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6896480321884155},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6373841762542725},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6100056767463684},{"id":"https://openalex.org/keywords/radial-basis-function-kernel","display_name":"Radial basis function kernel","score":0.5835160613059998},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.5572303533554077},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5445061922073364},{"id":"https://openalex.org/keywords/kernel-embedding-of-distributions","display_name":"Kernel embedding of distributions","score":0.5148312449455261},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4957486093044281},{"id":"https://openalex.org/keywords/variable-kernel-density-estimation","display_name":"Variable kernel density estimation","score":0.47808361053466797},{"id":"https://openalex.org/keywords/kernel-principal-component-analysis","display_name":"Kernel principal component analysis","score":0.4732775390148163},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.44907835125923157},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.42388346791267395},{"id":"https://openalex.org/keywords/polynomial-kernel","display_name":"Polynomial kernel","score":0.42184579372406006},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3727870583534241},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3626590073108673},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32432591915130615}],"concepts":[{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.8404484391212463},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6896480321884155},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6373841762542725},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6100056767463684},{"id":"https://openalex.org/C75866337","wikidata":"https://www.wikidata.org/wiki/Q7280263","display_name":"Radial basis function kernel","level":4,"score":0.5835160613059998},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.5572303533554077},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5445061922073364},{"id":"https://openalex.org/C134517425","wikidata":"https://www.wikidata.org/wiki/Q16000131","display_name":"Kernel embedding of distributions","level":4,"score":0.5148312449455261},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4957486093044281},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.47808361053466797},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.4732775390148163},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.44907835125923157},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.42388346791267395},{"id":"https://openalex.org/C160446489","wikidata":"https://www.wikidata.org/wiki/Q7226642","display_name":"Polynomial kernel","level":4,"score":0.42184579372406006},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3727870583534241},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3626590073108673},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32432591915130615},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ukci.2014.6930159","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ukci.2014.6930159","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W70833783","https://openalex.org/W97068511","https://openalex.org/W173069425","https://openalex.org/W1503398984","https://openalex.org/W1505510310","https://openalex.org/W1506281249","https://openalex.org/W1510073064","https://openalex.org/W1562135275","https://openalex.org/W1912123407","https://openalex.org/W1974799041","https://openalex.org/W1991699532","https://openalex.org/W2001461029","https://openalex.org/W2019044526","https://openalex.org/W2021091247","https://openalex.org/W2049701820","https://openalex.org/W2086829791","https://openalex.org/W2101057528","https://openalex.org/W2106053110","https://openalex.org/W2121249652","https://openalex.org/W2122111042","https://openalex.org/W2133324003","https://openalex.org/W2133990480","https://openalex.org/W2147169507","https://openalex.org/W2149298154","https://openalex.org/W2189004000","https://openalex.org/W2799061466","https://openalex.org/W3139701703","https://openalex.org/W4230946174","https://openalex.org/W6602877399","https://openalex.org/W6630164814","https://openalex.org/W6633773166","https://openalex.org/W6675576870","https://openalex.org/W6675751002","https://openalex.org/W6686613007"],"related_works":["https://openalex.org/W2393746448","https://openalex.org/W1984421104","https://openalex.org/W2071590642","https://openalex.org/W2384322977","https://openalex.org/W2512565647","https://openalex.org/W2366185040","https://openalex.org/W2127229869","https://openalex.org/W2027376491","https://openalex.org/W2164869055","https://openalex.org/W1636580594"],"abstract_inverted_index":{"Kernel-based":[0],"methods":[1,17],"have":[2],"become":[3],"popular":[4],"in":[5,20,123],"machine":[6],"learning;":[7],"however,":[8],"they":[9],"are":[10,18,24],"typically":[11],"designed":[12],"for":[13,26],"numeric":[14],"data.":[15,28,105],"These":[16],"established":[19],"vector":[21],"spaces,":[22],"which":[23],"undefined":[25],"categorical":[27,44,60,104],"In":[29],"this":[30,117],"paper,":[31],"we":[32],"propose":[33],"a":[34,55],"new":[35],"kind":[36],"of":[37,43,62,92,116],"kernel":[38,47,81],"trick,":[39],"showing":[40],"that":[41,67,121],"mapping":[42],"samples":[45],"into":[46],"spaces":[48],"can":[49,71],"be":[50,72],"alternatively":[51],"described":[52],"as":[53],"assigning":[54],"kernel-based":[56,94],"weight":[57],"to":[58,80,97,102,120],"each":[59],"attribute":[61],"the":[63,93,113,124],"input":[64,126],"space,":[65],"so":[66],"common":[68],"distance":[69,95],"measures":[70],"employed.":[73],"A":[74],"data-driven":[75],"approach":[76,118],"is":[77],"then":[78],"proposed":[79],"bandwidth":[82],"selection":[83],"by":[84],"optimizing":[85],"feature":[86],"weights.":[87],"We":[88],"also":[89],"make":[90],"use":[91],"measure":[96],"effectively":[98],"extend":[99],"nearest-neighbor":[100],"classification":[101],"classify":[103],"Experimental":[106],"results":[107],"on":[108],"real-world":[109],"data":[110],"sets":[111],"show":[112],"outstanding":[114],"performance":[115],"compared":[119],"obtained":[122],"original":[125],"space.":[127]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}