{"id":"https://openalex.org/W2787261406","doi":"https://doi.org/10.1109/iwbis.2017.8275104","title":"Optimization of Stacked Unsupervised Extreme Learning Machine to improve classifier performance","display_name":"Optimization of Stacked Unsupervised Extreme Learning Machine to improve classifier performance","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2787261406","doi":"https://doi.org/10.1109/iwbis.2017.8275104","mag":"2787261406"},"language":"en","primary_location":{"id":"doi:10.1109/iwbis.2017.8275104","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwbis.2017.8275104","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Workshop on Big Data and Information Security (IWBIS)","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/A5058432307","display_name":"Dewa Made Sri Arsa","orcid":"https://orcid.org/0000-0002-6558-2457"},"institutions":[{"id":"https://openalex.org/I29617571","display_name":"University of Indonesia","ror":"https://ror.org/0116zj450","country_code":"ID","type":"education","lineage":["https://openalex.org/I29617571"]}],"countries":["ID"],"is_corresponding":true,"raw_author_name":"Dewa Made Sri Arsa","raw_affiliation_strings":["Universitas Indonesia, Depok, Jawa Barat, ID"],"affiliations":[{"raw_affiliation_string":"Universitas Indonesia, Depok, Jawa Barat, ID","institution_ids":["https://openalex.org/I29617571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048298559","display_name":"M. Anwar Ma\u2019sum","orcid":"https://orcid.org/0000-0002-9251-7781"},"institutions":[{"id":"https://openalex.org/I29617571","display_name":"University of Indonesia","ror":"https://ror.org/0116zj450","country_code":"ID","type":"education","lineage":["https://openalex.org/I29617571"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"M. Anwar Ma'sum","raw_affiliation_strings":["Faculty of Computer Science, Universitas Indonesia, Indonesia"],"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science, Universitas Indonesia, Indonesia","institution_ids":["https://openalex.org/I29617571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037666948","display_name":"Muhammad Febrian Rachmadi","orcid":"https://orcid.org/0000-0003-1672-9149"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Muhammad Febrian Rachmadi","raw_affiliation_strings":["School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom"],"affiliations":[{"raw_affiliation_string":"School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069933043","display_name":"Wisnu Jatmiko","orcid":"https://orcid.org/0000-0002-0530-7955"},"institutions":[{"id":"https://openalex.org/I29617571","display_name":"University of Indonesia","ror":"https://ror.org/0116zj450","country_code":"ID","type":"education","lineage":["https://openalex.org/I29617571"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Wisnu Jatmiko","raw_affiliation_strings":["Faculty of Computer Science, Universitas Indonesia, Indonesia"],"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science, Universitas Indonesia, Indonesia","institution_ids":["https://openalex.org/I29617571"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5058432307"],"corresponding_institution_ids":["https://openalex.org/I29617571"],"apc_list":null,"apc_paid":null,"fwci":0.9751,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.82957476,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"63","last_page":"68"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","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/T12676","display_name":"Machine Learning and ELM","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/T10057","display_name":"Face and Expression Recognition","score":0.9941999912261963,"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/T10320","display_name":"Neural Networks and Applications","score":0.9882000088691711,"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/computer-science","display_name":"Computer science","score":0.7416430711746216},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5801555514335632},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5792586207389832},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5402289628982544},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5276236534118652},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.4973130524158478},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3649775981903076},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1915769875049591}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7416430711746216},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5801555514335632},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5792586207389832},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5402289628982544},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5276236534118652},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.4973130524158478},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3649775981903076},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1915769875049591}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwbis.2017.8275104","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwbis.2017.8275104","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Workshop on Big Data and Information Security (IWBIS)","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":31,"referenced_works":["https://openalex.org/W1710978090","https://openalex.org/W1874498466","https://openalex.org/W1980713635","https://openalex.org/W2019607443","https://openalex.org/W2033179381","https://openalex.org/W2036785686","https://openalex.org/W2042184006","https://openalex.org/W2078622091","https://openalex.org/W2092781199","https://openalex.org/W2097308346","https://openalex.org/W2109943925","https://openalex.org/W2115077553","https://openalex.org/W2119821739","https://openalex.org/W2134603844","https://openalex.org/W2135248726","https://openalex.org/W2153635508","https://openalex.org/W2171366262","https://openalex.org/W2283608423","https://openalex.org/W2285924575","https://openalex.org/W2301541953","https://openalex.org/W2380669522","https://openalex.org/W2417734945","https://openalex.org/W2472996304","https://openalex.org/W2605495192","https://openalex.org/W2614326984","https://openalex.org/W2739601332","https://openalex.org/W2741133309","https://openalex.org/W3120421331","https://openalex.org/W4239510810","https://openalex.org/W6637642295","https://openalex.org/W6685524898"],"related_works":["https://openalex.org/W2969890106","https://openalex.org/W3196155444","https://openalex.org/W328659180","https://openalex.org/W4321844043","https://openalex.org/W3210156800","https://openalex.org/W4390062853","https://openalex.org/W4297883248","https://openalex.org/W4255830763","https://openalex.org/W1583266947","https://openalex.org/W4286799911"],"abstract_inverted_index":{"In":[0,101],"the":[1,32,35,57,62,75,95,99,106,136,144,156,162,186,190,193,198,203,209,213,227],"era":[2],"of":[3,59,74,98,108,131,138,140,158,192,224,226],"Big":[4],"Data,":[5],"data":[6,9,25,29,49,53,171],"size":[7],"and":[8,51,119,151,161,202,234,249],"security":[10],"are":[11,230],"issues":[12],"that":[13],"need":[14,92],"to":[15,82,93,134,169,184],"be":[16,40,80],"solved.":[17],"To":[18,154],"address":[19,83],"this":[20,102],"problem,":[21],"we":[22,91,104,165],"may":[23,39,47],"apply":[24],"compression":[26],"technique":[27],"or":[28],"encryption.":[30],"On":[31],"other":[33,41],"hand,":[34],"coding":[36,44],"based":[37,45],"method":[38,46,126,211,229],"solution.":[42],"The":[43,85,124,178,206,222],"learn":[48],"distribution":[50],"reduce":[52],"dimension":[54],"with":[55,197],"minimized":[56],"loss":[58],"information":[60],"from":[61],"original":[63],"data.":[64],"Stacked":[65,109,132,159,200,217],"Unsupervised":[66,110],"Extreme":[67,111],"Learning":[68,112],"Machine":[69,113,176],"(Stacked":[70],"US-ELM)":[71],"is":[72,87,127,182],"one":[73],"fastest":[76],"methods":[77],"which":[78],"can":[79],"used":[81,183],"it.":[84],"problem":[86],"how":[88],"many":[89],"stacks":[90,139],"get":[94],"optimal":[96],"performance":[97,107,118,157,215],"classifier.":[100],"research,":[103],"inspected":[105],"(US-ELM)":[114],"for":[115],"enhancing":[116],"classifier":[117],"proposed":[120,125,163,204,210,228],"a":[121,128],"new":[122],"method.":[123,205],"loop":[129],"scheme":[130],"US-ELM":[133,160,196,201,218],"optimize":[135],"number":[137],"US-ELM.":[141],"We":[142,188],"conducted":[143,166],"experiment":[145],"using":[146,173],"ECG-sleep":[147,240,244],"dataset,":[148,150,241,245,248],"synthetic":[149,247],"Glass":[152],"dataset.":[153,221,251],"measure":[155],"method,":[164],"classification":[167,194],"according":[168],"our":[170],"sets":[172],"Support":[174],"Vector":[175],"(SVM).":[177],"5-Folds":[179],"Cross":[180],"Validation":[181],"evaluate":[185],"performance.":[187],"compared":[189],"result":[191],"without":[195],"fix":[199],"results":[207],"showed":[208],"achieved":[212],"best":[214],"over":[216],"in":[219],"all":[220],"mean":[223],"accuracies":[225],"64,39%,":[231],"76,24%,":[232],"63,22%,":[233],"69,":[235],"61%":[236],"on":[237],"4":[238],"class":[239,243],"3":[242],"skewed":[246],"glass":[250]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
