{"id":"https://openalex.org/W4226312345","doi":"https://doi.org/10.1109/tnsm.2022.3180936","title":"Online Feature Selection for Efficient Learning in Networked Systems","display_name":"Online Feature Selection for Efficient Learning in Networked Systems","publication_year":2022,"publication_date":"2022-06-08","ids":{"openalex":"https://openalex.org/W4226312345","doi":"https://doi.org/10.1109/tnsm.2022.3180936"},"language":"en","primary_location":{"id":"doi:10.1109/tnsm.2022.3180936","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tnsm.2022.3180936","pdf_url":"https://ieeexplore.ieee.org/ielx7/4275028/9917435/09790320.pdf","source":{"id":"https://openalex.org/S173527311","display_name":"IEEE Transactions on Network and Service Management","issn_l":"1932-4537","issn":["1932-4537","2373-7379"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Network and Service Management","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://ieeexplore.ieee.org/ielx7/4275028/9917435/09790320.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003853801","display_name":"Xiaoxuan Wang","orcid":"https://orcid.org/0000-0002-2414-3108"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Xiaoxuan Wang","raw_affiliation_strings":["Department of Computer Science, Digital Futures, KTH Royal Institute of Technology, Stockholm, Sweden"],"raw_orcid":"https://orcid.org/0000-0002-2414-3108","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Digital Futures, KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035040105","display_name":"Rolf Stadler","orcid":"https://orcid.org/0000-0001-6039-8493"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Rolf Stadler","raw_affiliation_strings":["Department of Computer Science, Digital Futures, KTH Royal Institute of Technology, Stockholm, Sweden"],"raw_orcid":"https://orcid.org/0000-0001-6039-8493","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Digital Futures, KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I86987016"],"apc_list":null,"apc_paid":{"value":1165,"currency":"EUR","value_usd":1256},"fwci":0.3936,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.65224549,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"19","issue":"3","first_page":"2885","last_page":"2898"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","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/T12761","display_name":"Data Stream Mining Techniques","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/T12535","display_name":"Machine Learning and Data Classification","score":0.9941999912261963,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9835000038146973,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.7700273394584656},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.756274402141571},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.687666654586792},{"id":"https://openalex.org/keywords/testbed","display_name":"Testbed","score":0.6722327470779419},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5941576361656189},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5502878427505493},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5041564702987671},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.5008606910705566},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.4980952739715576},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4890750050544739},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4371837377548218},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.42247438430786133},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37496212124824524}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7700273394584656},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.756274402141571},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.687666654586792},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.6722327470779419},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5941576361656189},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5502878427505493},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5041564702987671},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5008606910705566},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.4980952739715576},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4890750050544739},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4371837377548218},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.42247438430786133},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37496212124824524},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tnsm.2022.3180936","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tnsm.2022.3180936","pdf_url":"https://ieeexplore.ieee.org/ielx7/4275028/9917435/09790320.pdf","source":{"id":"https://openalex.org/S173527311","display_name":"IEEE Transactions on Network and Service Management","issn_l":"1932-4537","issn":["1932-4537","2373-7379"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Network and Service Management","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/tnsm.2022.3180936","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tnsm.2022.3180936","pdf_url":"https://ieeexplore.ieee.org/ielx7/4275028/9917435/09790320.pdf","source":{"id":"https://openalex.org/S173527311","display_name":"IEEE Transactions on Network and Service Management","issn_l":"1932-4537","issn":["1932-4537","2373-7379"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Network and Service Management","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320314279","display_name":"Aniara Diagnostica","ror":null},{"id":"https://openalex.org/F4320321030","display_name":"VINNOVA","ror":"https://ror.org/01kd5m353"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4226312345.pdf","grobid_xml":"https://content.openalex.org/works/W4226312345.grobid-xml"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W1475575984","https://openalex.org/W1574249233","https://openalex.org/W1887132526","https://openalex.org/W1993573509","https://openalex.org/W2051903196","https://openalex.org/W2099419573","https://openalex.org/W2119885577","https://openalex.org/W2136051823","https://openalex.org/W2145511633","https://openalex.org/W2149620660","https://openalex.org/W2153338628","https://openalex.org/W2154461858","https://openalex.org/W2167101736","https://openalex.org/W2294370754","https://openalex.org/W2527508530","https://openalex.org/W2609002182","https://openalex.org/W2752162157","https://openalex.org/W2757551206","https://openalex.org/W2758096849","https://openalex.org/W2758219826","https://openalex.org/W2801183640","https://openalex.org/W2847284300","https://openalex.org/W2906939822","https://openalex.org/W2911627187","https://openalex.org/W2912882859","https://openalex.org/W2921881548","https://openalex.org/W2955612236","https://openalex.org/W2997416769","https://openalex.org/W2998216295","https://openalex.org/W3010335356","https://openalex.org/W3036781224","https://openalex.org/W3080633080","https://openalex.org/W3092048541","https://openalex.org/W3108835755","https://openalex.org/W3134696756","https://openalex.org/W3135908355","https://openalex.org/W3136654195","https://openalex.org/W3161172664","https://openalex.org/W3182057971","https://openalex.org/W4254721730","https://openalex.org/W4283220631","https://openalex.org/W4285058377","https://openalex.org/W4288095730","https://openalex.org/W4289236186","https://openalex.org/W6634364709","https://openalex.org/W6639167513","https://openalex.org/W6681822384","https://openalex.org/W6737135848","https://openalex.org/W6749067469","https://openalex.org/W6752866118","https://openalex.org/W6775263099","https://openalex.org/W6791059233","https://openalex.org/W7042291767","https://openalex.org/W7066667914"],"related_works":["https://openalex.org/W2883256816","https://openalex.org/W2171408034","https://openalex.org/W3003320923","https://openalex.org/W2106140982","https://openalex.org/W2152313554","https://openalex.org/W2064303750","https://openalex.org/W51364034","https://openalex.org/W2141938446","https://openalex.org/W2898073868","https://openalex.org/W4284663758"],"abstract_inverted_index":{"Current":[0],"AI/ML":[1],"methods":[2],"for":[3,68,220],"data-driven":[4],"engineering":[5],"use":[6],"models":[7,14],"that":[8,32,59,144,170],"are":[9],"mostly":[10],"trained":[11,176,190],"offline.":[12],"Such":[13],"can":[15,239],"be":[16,205,240],"expensive":[17],"to":[18,204],"build":[19],"in":[20,46,150,230],"terms":[21],"of":[22,38,64,93,102,128,153,160,173],"communication":[23],"and":[24,27,118,137,213,247],"computing":[25],"costs,":[26],"they":[28,41],"rely":[29],"on":[30,162,177,191],"data":[31,66,95,232],"is":[33,106,182,189,201],"collected":[34],"over":[35],"extended":[36],"periods":[37],"time.":[39],"Further,":[40],"become":[42],"out-of-date":[43],"when":[44,186,227],"changes":[45],"the":[47,62,151,154,171,187,216,231,234,244,249],"system":[48],"occur.":[49],"To":[50],"address":[51],"these":[52],"challenges,":[53],"we":[54,168],"investigate":[55],"online":[56,74,209],"learning":[57],"techniques":[58],"automatically":[60],"reduce":[61],"number":[63,92,101],"available":[65,94],"sources":[67,96],"model":[69,235],"training.":[70],"We":[71,122,142,223],"present":[72],"an":[73,124,134,208],"algorithm":[75,105,130,212],"called":[76],"Online":[77],"Stable":[78],"Feature":[79],"Set":[80],"Algorithm":[81],"(OSFS),":[82],"which":[83],"selects":[84],"a":[85,90,99,109,113,119,147,174,178,192],"small":[86,100],"feature":[87,110,114,155,180,193,198,210,221,245],"set":[88,115,156,181,194,246],"from":[89,133,138],"large":[91],"after":[97],"receiving":[98],"measurements.":[103],"The":[104],"initialized":[107],"with":[108],"ranking":[111],"algorithm,":[112],"stability":[116],"metric,":[117],"search":[120],"policy.":[121],"perform":[123],"extensive":[125],"experimental":[126],"evaluation":[127],"this":[129],"using":[131],"traces":[132],"in-house":[135],"testbed":[136],"two":[139],"external":[140],"datasets.":[141,165],"find":[143,169,225],"OSFS":[145,200],"achieves":[146],"massive":[148],"reduction":[149],"size":[152],"by":[157,242],"1\u20133":[158],"orders":[159],"magnitude":[161],"all":[163],"investigated":[164],"Most":[166],"importantly,":[167],"accuracy":[172],"predictor":[175,188],"OSFS-produced":[179],"somewhat":[183],"better":[184],"than":[185],"obtained":[195],"through":[196],"offline":[197],"selection.":[199,222],"thus":[202],"shown":[203],"effective":[206],"as":[207],"selection":[211],"robust":[214],"regarding":[215],"sample":[217],"interval":[218],"used":[219],"also":[224],"that,":[226],"concept":[228],"drift":[229],"underlying":[233],"occurs,":[236],"its":[237],"effect":[238],"mitigated":[241],"recomputing":[243],"retraining":[248],"prediction":[250],"model.":[251]},"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"}