{"id":"https://openalex.org/W4391893128","doi":"https://doi.org/10.1145/3639631.3639651","title":"Multiple Kernel Learning for Learner Classification","display_name":"Multiple Kernel Learning for Learner Classification","publication_year":2023,"publication_date":"2023-12-22","ids":{"openalex":"https://openalex.org/W4391893128","doi":"https://doi.org/10.1145/3639631.3639651"},"language":"en","primary_location":{"id":"doi:10.1145/3639631.3639651","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3639631.3639651","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 6th International Conference on Algorithms Computing and Artificial Intelligence","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/A5081368477","display_name":"Yuan Liang","orcid":"https://orcid.org/0009-0007-6669-9482"},"institutions":[{"id":"https://openalex.org/I921716337","display_name":"Northeast Petroleum University","ror":"https://ror.org/03net5943","country_code":"CN","type":"education","lineage":["https://openalex.org/I921716337"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuan Liang","raw_affiliation_strings":["College of Computer and Information Technology, Northeast Petroleum University, China"],"affiliations":[{"raw_affiliation_string":"College of Computer and Information Technology, Northeast Petroleum University, China","institution_ids":["https://openalex.org/I921716337"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062009904","display_name":"Mei Wang","orcid":"https://orcid.org/0000-0002-3759-1719"},"institutions":[{"id":"https://openalex.org/I921716337","display_name":"Northeast Petroleum University","ror":"https://ror.org/03net5943","country_code":"CN","type":"education","lineage":["https://openalex.org/I921716337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mei Wang","raw_affiliation_strings":["College of Computer and Information Technology, Northeast Petroleum University, China"],"affiliations":[{"raw_affiliation_string":"College of Computer and Information Technology, Northeast Petroleum University, China","institution_ids":["https://openalex.org/I921716337"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5081368477"],"corresponding_institution_ids":["https://openalex.org/I921716337"],"apc_list":null,"apc_paid":null,"fwci":0.1625,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.56060606,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"113","last_page":"118"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9904000163078308,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9836000204086304,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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":0.7651941776275635},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5973647832870483},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5531512498855591},{"id":"https://openalex.org/keywords/multiple-kernel-learning","display_name":"Multiple kernel learning","score":0.47111988067626953},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46066370606422424},{"id":"https://openalex.org/keywords/tree-kernel","display_name":"Tree kernel","score":0.41108042001724243},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.33385351300239563},{"id":"https://openalex.org/keywords/polynomial-kernel","display_name":"Polynomial kernel","score":0.33109545707702637},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.23106169700622559},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12137144804000854}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7651941776275635},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5973647832870483},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5531512498855591},{"id":"https://openalex.org/C2776879701","wikidata":"https://www.wikidata.org/wiki/Q25048660","display_name":"Multiple kernel learning","level":4,"score":0.47111988067626953},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46066370606422424},{"id":"https://openalex.org/C140417398","wikidata":"https://www.wikidata.org/wiki/Q16933942","display_name":"Tree kernel","level":5,"score":0.41108042001724243},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.33385351300239563},{"id":"https://openalex.org/C160446489","wikidata":"https://www.wikidata.org/wiki/Q7226642","display_name":"Polynomial kernel","level":4,"score":0.33109545707702637},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.23106169700622559},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12137144804000854},{"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:10.1145/3639631.3639651","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3639631.3639651","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 6th International Conference on Algorithms Computing and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.8399999737739563,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W2145295623","https://openalex.org/W2146611644","https://openalex.org/W2164330327","https://openalex.org/W6819677840"],"related_works":["https://openalex.org/W2096302783","https://openalex.org/W1983263273","https://openalex.org/W3123056048","https://openalex.org/W2179275589","https://openalex.org/W2089892314","https://openalex.org/W2974741803","https://openalex.org/W2095626363","https://openalex.org/W2167608136","https://openalex.org/W4291669689","https://openalex.org/W2001173190"],"abstract_inverted_index":{"Abstract:At":[0],"present,":[1],"educational":[2],"researchers":[3],"are":[4,85],"continuously":[5],"exploring":[6],"new":[7],"ways":[8],"of":[9,16,36,46,74,114,124,139,148],"learner":[10,26,90,154],"models.":[11],"To":[12],"solve":[13],"the":[14,34,44,61,66,72,102,107,112,122,125,146,153],"problems":[15],"data":[17],"bias":[18],"and":[19,50,81,109],"feature":[20],"mismatch":[21],"that":[22,64,121,138,145],"may":[23],"occur":[24],"in":[25,152],"modeling":[27],"on":[28,69,106,132],"heterogeneous":[29],"data,":[30],"this":[31],"paper":[32],"uses":[33],"method":[35,147],"Multiple":[37],"Kernel":[38],"Learning":[39],"(MKL)":[40],"to":[41],"accurately":[42],"portray":[43],"features":[45,73],"learners'":[47],"thinking":[48,62,76,79,83],"skills":[49,63,80,84],"construct":[51],"a":[52,89],"classification":[53,91,126,155],"model":[54,92,103,127],"for":[55,93,128],"top":[56,70,94,129],"talents.":[57],"First,":[58],"by":[59,98],"analyzing":[60],"have":[65],"greatest":[67],"impact":[68],"talents,":[71],"critical":[75],"skills,":[77],"logical":[78],"computational":[82],"extracted":[86],"separately.":[87],"Then,":[88],"talents":[95,130],"was":[96,104],"constructed":[97],"MKL":[99,133,149],"method.":[100],"Finally,":[101],"validated":[105],"dataset":[108],"compared":[110],"with":[111],"results":[113,119],"single-kernel":[115,141],"learning.":[116],"The":[117],"experimental":[118],"show":[120],"accuracy":[123],"based":[131],"is":[134],"significantly":[135],"better":[136,151],"than":[137],"ordinary":[140],"learning,":[142],"which":[143],"indicates":[144],"performs":[150],"model.":[156]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
