{"id":"https://openalex.org/W2174687791","doi":"https://doi.org/10.1109/fuzz-ieee.2015.7337844","title":"Efficient sample selection in data stream regression employing evolving generalized fuzzy models","display_name":"Efficient sample selection in data stream regression employing evolving generalized fuzzy models","publication_year":2015,"publication_date":"2015-08-01","ids":{"openalex":"https://openalex.org/W2174687791","doi":"https://doi.org/10.1109/fuzz-ieee.2015.7337844","mag":"2174687791"},"language":"en","primary_location":{"id":"doi:10.1109/fuzz-ieee.2015.7337844","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz-ieee.2015.7337844","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5010578654","display_name":"Edwin Lughofer","orcid":"https://orcid.org/0000-0003-1560-5136"},"institutions":[{"id":"https://openalex.org/I121883995","display_name":"Johannes Kepler University of Linz","ror":"https://ror.org/052r2xn60","country_code":"AT","type":"education","lineage":["https://openalex.org/I121883995"]}],"countries":["AT"],"is_corresponding":true,"raw_author_name":"Edwin Lughofer","raw_affiliation_strings":["Department of Knowledge-Based Mathematical Systems, Johannes Kepler University of Linz, Linz, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Knowledge-Based Mathematical Systems, Johannes Kepler University of Linz, Linz, Austria","institution_ids":["https://openalex.org/I121883995"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5010578654"],"corresponding_institution_ids":["https://openalex.org/I121883995"],"apc_list":null,"apc_paid":null,"fwci":0.2058,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.45723093,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9994999766349792,"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":0.9994999766349792,"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/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.996999979019165,"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/T12135","display_name":"Fuzzy Systems and Optimization","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/extrapolation","display_name":"Extrapolation","score":0.7068196535110474},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.655785858631134},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5924564003944397},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.5472445487976074},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5150975584983826},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4826850891113281},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4805181920528412},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.44974732398986816},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4281178116798401},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.4276721477508545},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3073405623435974},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3050583004951477},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.24261420965194702}],"concepts":[{"id":"https://openalex.org/C132459708","wikidata":"https://www.wikidata.org/wiki/Q744069","display_name":"Extrapolation","level":2,"score":0.7068196535110474},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.655785858631134},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5924564003944397},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.5472445487976074},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5150975584983826},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4826850891113281},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4805181920528412},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.44974732398986816},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4281178116798401},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.4276721477508545},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3073405623435974},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3050583004951477},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24261420965194702},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fuzz-ieee.2015.7337844","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz-ieee.2015.7337844","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W821107","https://openalex.org/W43637044","https://openalex.org/W178056938","https://openalex.org/W301800434","https://openalex.org/W322952782","https://openalex.org/W1479807131","https://openalex.org/W1520101322","https://openalex.org/W1537430301","https://openalex.org/W1542883383","https://openalex.org/W1554944419","https://openalex.org/W1965324089","https://openalex.org/W1999649259","https://openalex.org/W1999860347","https://openalex.org/W2009345066","https://openalex.org/W2015630349","https://openalex.org/W2021329299","https://openalex.org/W2026386069","https://openalex.org/W2028549589","https://openalex.org/W2029174532","https://openalex.org/W2030950474","https://openalex.org/W2037768666","https://openalex.org/W2049628044","https://openalex.org/W2050852806","https://openalex.org/W2052345845","https://openalex.org/W2057974719","https://openalex.org/W2059207720","https://openalex.org/W2073102330","https://openalex.org/W2079325629","https://openalex.org/W2086945083","https://openalex.org/W2093323445","https://openalex.org/W2093768254","https://openalex.org/W2100157781","https://openalex.org/W2108740451","https://openalex.org/W2109205796","https://openalex.org/W2114338449","https://openalex.org/W2125667824","https://openalex.org/W2128718601","https://openalex.org/W2135106139","https://openalex.org/W2135335717","https://openalex.org/W2151023586","https://openalex.org/W2151863350","https://openalex.org/W2153196467","https://openalex.org/W2162635690","https://openalex.org/W2166777045","https://openalex.org/W2171033594","https://openalex.org/W2293050121","https://openalex.org/W2469497343","https://openalex.org/W2480422823","https://openalex.org/W2500228126","https://openalex.org/W2537520019","https://openalex.org/W2544863478","https://openalex.org/W2903158431","https://openalex.org/W3003253354","https://openalex.org/W4230030242","https://openalex.org/W4234672050","https://openalex.org/W4250364582","https://openalex.org/W6677284661","https://openalex.org/W6678630487","https://openalex.org/W6680192438","https://openalex.org/W6680272842","https://openalex.org/W6756615331"],"related_works":["https://openalex.org/W4296478327","https://openalex.org/W2042397106","https://openalex.org/W2333625343","https://openalex.org/W4361730764","https://openalex.org/W1965029248","https://openalex.org/W2786502182","https://openalex.org/W1960072520","https://openalex.org/W2220129715","https://openalex.org/W2023776155","https://openalex.org/W2075787792"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"two":[5,90,179],"criteria":[6,87,136],"for":[7,48],"efficient":[8],"sample":[9],"selection":[10,19,86,135],"in":[11,73,107,114,139,150,172],"case":[12,215],"of":[13,30,60,96,116,188,191,200,230,234],"data":[14],"stream":[15,169],"regression":[16,170],"problems.":[17],"The":[18,134,175],"becomes":[20],"apparent":[21],"whenever":[22,236],"the":[23,28,31,36,45,57,61,93,97,105,132,189,214],"target":[24],"values,":[25],"which":[26,110,125,154],"guide":[27],"update":[29,220],"regressors":[32],"as":[33,35,51,53],"well":[34],"implicit":[37],"model":[38,49,98,108,192,209],"structures,":[39],"are":[40,126,137,202,238],"costly":[41,239],"to":[42,131,158,213,240],"measure.":[43,241],"Reducing":[44],"samples":[46,201],"used":[47],"updates":[50,193],"much":[52],"possible":[54],"while":[55],"keeping":[56],"predictive":[58],"accuracy":[59],"models":[62,147,163],"on":[63,89,178,221],"a":[64,69,186,218,227],"high":[65],"level":[66],"is":[67],"thus":[68],"central":[70],"challenge,":[71],"especially":[72],"non-stationary":[74],"environments":[75],"where":[76],"(permanent)":[77],"system":[78],"changes":[79],"or":[80],"expansion":[81],"can":[82,111,204],"be":[83,112,156],"expected.":[84],"Our":[85],"rely":[88],"aspects:":[91],"1.)":[92],"extrapolation":[94],"degree":[95],"combined":[99],"with":[100,141],"its":[101],"non-linearity":[102],"degree,":[103],"2.)":[104],"uncertainty":[106],"outputs":[109],"measured":[113],"terms":[115],"confidence":[117],"intervals":[118],"reflected":[119],"by":[120,194],"so-called":[121],"adaptive":[122],"error":[123],"bars,":[124],"updated":[127],"over":[128,211],"time":[129,212],"synchronously":[130],"model.":[133],"developed":[138],"combination":[140],"evolving":[142,161],"generalized":[143],"Takagi-Sugeno":[144],"(TS)":[145],"fuzzy":[146],"(containing":[148,164],"rules":[149],"arbitrarily":[151],"rotated":[152],"position),":[153],"could":[155],"shown":[157],"outperform":[159],"conventional":[160],"TS":[162],"axis-parallel":[165],"rules)":[166],"and":[167,233],"other":[168],"techniques":[171],"previous":[173],"publications.":[174],"results":[176],"based":[177],"high-dimensional":[180],"real-world":[181],"streaming":[182],"problems":[183],"show":[184],"that":[185],"decrease":[187],"number":[190],"about":[195],"80-85%":[196],"(as":[197],"only":[198],"15-20%":[199],"selected)":[203],"still":[205],"achieve":[206],"similar":[207],"accumulated":[208],"errors":[210],"when":[216],"performing":[217],"full":[219],"all":[222],"samples.":[223],"This":[224],"may":[225],"yield":[226],"significant":[228],"reduction":[229],"computational":[231],"demands":[232],"costs":[235],"targets":[237]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}