{"id":"https://openalex.org/W4364302687","doi":"https://doi.org/10.1109/tnnls.2023.3263506","title":"Metafeature Selection via Multivariate Sparse-Group Lasso Learning for Automatic Hyperparameter Configuration Recommendation","display_name":"Metafeature Selection via Multivariate Sparse-Group Lasso Learning for Automatic Hyperparameter Configuration Recommendation","publication_year":2023,"publication_date":"2023-04-10","ids":{"openalex":"https://openalex.org/W4364302687","doi":"https://doi.org/10.1109/tnnls.2023.3263506","pmid":"https://pubmed.ncbi.nlm.nih.gov/37037247"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2023.3263506","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2023.3263506","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5006353150","display_name":"Liping Deng","orcid":"https://orcid.org/0000-0001-6903-3239"},"institutions":[{"id":"https://openalex.org/I110378019","display_name":"Southern Illinois University Carbondale","ror":"https://ror.org/049kefs16","country_code":"US","type":"education","lineage":["https://openalex.org/I110378019","https://openalex.org/I2801502357"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Liping Deng","raw_affiliation_strings":["School of Mathematical and Statistical Sciences, Southern Illinois University Carbondale, Carbondale, IL, USA"],"affiliations":[{"raw_affiliation_string":"School of Mathematical and Statistical Sciences, Southern Illinois University Carbondale, Carbondale, IL, USA","institution_ids":["https://openalex.org/I110378019"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053712480","display_name":"Wensheng Chen","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":"Wen-Sheng Chen","raw_affiliation_strings":["College of Mathematics and Statistics, Shenzhen University, Shenzhen, Guangdong, China"],"affiliations":[{"raw_affiliation_string":"College of Mathematics and Statistics, Shenzhen University, Shenzhen, Guangdong, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008007155","display_name":"Mingqing Xiao","orcid":"https://orcid.org/0000-0003-3241-4112"},"institutions":[{"id":"https://openalex.org/I110378019","display_name":"Southern Illinois University Carbondale","ror":"https://ror.org/049kefs16","country_code":"US","type":"education","lineage":["https://openalex.org/I110378019","https://openalex.org/I2801502357"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mingqing Xiao","raw_affiliation_strings":["School of Mathematical and Statistical Sciences, Southern Illinois University Carbondale, Carbondale, IL, USA"],"affiliations":[{"raw_affiliation_string":"School of Mathematical and Statistical Sciences, Southern Illinois University Carbondale, Carbondale, IL, USA","institution_ids":["https://openalex.org/I110378019"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5006353150"],"corresponding_institution_ids":["https://openalex.org/I110378019"],"apc_list":null,"apc_paid":null,"fwci":0.5245,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.69505976,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"35","issue":"9","first_page":"12540","last_page":"12552"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9890000224113464,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9890000224113464,"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/hyperparameter","display_name":"Hyperparameter","score":0.9470363855361938},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7274624109268188},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6934966444969177},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6550716161727905},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.6439914107322693},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5301077365875244},{"id":"https://openalex.org/keywords/lasso","display_name":"Lasso (programming language)","score":0.5297924876213074},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5073537230491638},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.47906264662742615},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4276849925518036},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3808198869228363}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.9470363855361938},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7274624109268188},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6934966444969177},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6550716161727905},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.6439914107322693},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5301077365875244},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.5297924876213074},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5073537230491638},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.47906264662742615},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4276849925518036},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3808198869228363},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2023.3263506","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2023.3263506","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:37037247","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37037247","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W1494580925","https://openalex.org/W1504618398","https://openalex.org/W1507030697","https://openalex.org/W1512022526","https://openalex.org/W1558866804","https://openalex.org/W1560240297","https://openalex.org/W1593343777","https://openalex.org/W1631708156","https://openalex.org/W1925504357","https://openalex.org/W1969163824","https://openalex.org/W1987371344","https://openalex.org/W1989048657","https://openalex.org/W1996452481","https://openalex.org/W2018449938","https://openalex.org/W2112204321","https://openalex.org/W2122379760","https://openalex.org/W2125536150","https://openalex.org/W2138019504","https://openalex.org/W2157686535","https://openalex.org/W2313022313","https://openalex.org/W2556522401","https://openalex.org/W2557223339","https://openalex.org/W2563364594","https://openalex.org/W2606436201","https://openalex.org/W2777539043","https://openalex.org/W2784287478","https://openalex.org/W2789857439","https://openalex.org/W2949694084","https://openalex.org/W2950220059","https://openalex.org/W2959482287","https://openalex.org/W2964002344","https://openalex.org/W2998993395","https://openalex.org/W2999917162","https://openalex.org/W3000276536","https://openalex.org/W3001998614","https://openalex.org/W3044965819","https://openalex.org/W3098453412","https://openalex.org/W3100203766","https://openalex.org/W3120740533","https://openalex.org/W3137665052","https://openalex.org/W4211208325","https://openalex.org/W4212914324","https://openalex.org/W4213353582","https://openalex.org/W4233454717","https://openalex.org/W4239510810","https://openalex.org/W4285043533","https://openalex.org/W4313442775","https://openalex.org/W6678911119","https://openalex.org/W6730169791","https://openalex.org/W6738227506","https://openalex.org/W6768177164"],"related_works":["https://openalex.org/W2953665647","https://openalex.org/W4281646320","https://openalex.org/W3169687406","https://openalex.org/W2954882791","https://openalex.org/W4205712847","https://openalex.org/W1974336862","https://openalex.org/W3014750173","https://openalex.org/W4287818966","https://openalex.org/W3192751261","https://openalex.org/W3200811867"],"abstract_inverted_index":{"The":[0,102,233],"performance":[1,122,138,179,197],"of":[2,30,36,57,139,142,147,164,180,200,229],"classification":[3,137,178],"algorithms":[4],"is":[5,23,105,155,216],"mainly":[6],"governed":[7],"by":[8,111],"the":[9,16,28,40,47,54,64,108,124,134,145,161,165,177,186,191,194,201,227,230,237,251,256],"hyperparameter":[10,20,125],"settings":[11],"deployed":[12],"in":[13,70,96,123,198],"applications,":[14],"and":[15,46,74,121,136,150,170,249,255,266],"search":[17],"for":[18],"desirable":[19],"configurations":[21,99,143,181,248],"usually":[22],"quite":[24],"challenging":[25],"due":[26],"to":[27,62,77,106,159],"complexity":[29],"datasets.":[31],"Metafeatures":[32],"are":[33,220],"a":[34,86,140,152,174,208],"group":[35],"measures":[37],"that":[38,190,241],"characterize":[39],"underlying":[41,166],"dataset":[42,203],"from":[43,144],"various":[44],"aspects,":[45],"corresponding":[48],"recommendation":[49],"algorithm":[50,66,75],"fully":[51],"relies":[52],"on":[53,222,236],"appropriate":[55],"selection":[56],"metafeatures.":[58],"Metalearning":[59],"(MtL),":[60],"aiming":[61],"improve":[63],"learning":[65,76],"itself,":[67],"requires":[68],"development":[69],"integrating":[71],"features,":[72],"models,":[73],"accomplish":[78],"its":[79],"goal.":[80],"In":[81],"this":[82],"article,":[83],"we":[84,131],"develop":[85],"multivariate":[87],"sparse-group":[88],"Lasso":[89],"(SGLasso)":[90],"model":[91,215,243],"embedded":[92],"with":[93,193,213],"MtL":[94,210],"capacity":[95],"recommending":[97],"suitable":[98,247],"via":[100],"learning.":[101],"main":[103,162],"idea":[104],"select":[107],"principal":[109],"metafeatures":[110,135,169,188],"removing":[112],"those":[113],"redundant":[114],"or":[115],"irregular":[116],"ones,":[117],"promoting":[118],"both":[119],"efficiency":[120],"configuration":[126,192],"recommendation.":[127],"To":[128],"be":[129,183,205],"specific,":[130],"first":[132],"extract":[133],"set":[141],"collection":[146],"historical":[148,171],"datasets,":[149,225],"then,":[151],"metaregression":[153],"task":[154],"established":[156],"through":[157,185],"SGLasso":[158],"capture":[160],"characteristics":[163],"relationship":[167],"between":[168],"performance.":[172],"For":[173],"new":[175,202],"dataset,":[176],"can":[182,204,244],"estimated":[184],"selected":[187],"so":[189],"highest":[195],"predictive":[196],"terms":[199],"generated.":[206],"Furthermore,":[207],"general":[209],"architecture":[211],"combined":[212],"our":[214,242],"developed.":[217],"Extensive":[218],"experiments":[219],"conducted":[221],"136":[223],"UCI":[224],"demonstrating":[226],"effectiveness":[228],"proposed":[231],"approach.":[232],"empirical":[234],"results":[235],"well-known":[238,257],"SVM":[239],"show":[240],"effectively":[245],"recommend":[246],"outperform":[250],"existing":[252],"MtL-based":[253],"methods":[254],"search-based":[258],"algorithms,":[259],"such":[260],"as":[261],"random":[262],"search,":[263],"Bayesian":[264],"optimization,":[265],"Hyperband.":[267]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
