{"id":"https://openalex.org/W2129940690","doi":"https://doi.org/10.1109/csndsp16145.2010.5580465","title":"Feature selection using an ensemble of optimal wavelet packet and learning machine: Application to MER signals","display_name":"Feature selection using an ensemble of optimal wavelet packet and learning machine: Application to MER signals","publication_year":2010,"publication_date":"2010-07-01","ids":{"openalex":"https://openalex.org/W2129940690","doi":"https://doi.org/10.1109/csndsp16145.2010.5580465","mag":"2129940690"},"language":"en","primary_location":{"id":"doi:10.1109/csndsp16145.2010.5580465","is_oa":false,"landing_page_url":"https://doi.org/10.1109/csndsp16145.2010.5580465","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 7th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP 2010)","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/A5049744324","display_name":"Ruben-Dario Pinzon-Morales","orcid":null},"institutions":[{"id":"https://openalex.org/I200183955","display_name":"Technological University of Pereira","ror":"https://ror.org/01d981710","country_code":"CO","type":"education","lineage":["https://openalex.org/I200183955"]}],"countries":["CO"],"is_corresponding":false,"raw_author_name":"R.D. Pinzon-Morales","raw_affiliation_strings":["Department of Electrical Engineering, Technical University of Pereira, Pereira, Colombia","Technological University of Pereira/ Department of Electrical Engineering, Colombia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Technical University of Pereira, Pereira, Colombia","institution_ids":["https://openalex.org/I200183955"]},{"raw_affiliation_string":"Technological University of Pereira/ Department of Electrical Engineering, Colombia","institution_ids":["https://openalex.org/I200183955"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012268339","display_name":"\u00c1lvaro A. Orozco","orcid":"https://orcid.org/0000-0002-1167-1446"},"institutions":[{"id":"https://openalex.org/I200183955","display_name":"Technological University of Pereira","ror":"https://ror.org/01d981710","country_code":"CO","type":"education","lineage":["https://openalex.org/I200183955"]}],"countries":["CO"],"is_corresponding":false,"raw_author_name":"A.A Orozco-Gutierrez","raw_affiliation_strings":["Colombia","Technological University of Pereira/ Department of Electrical Engineering, Colombia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Colombia","institution_ids":[]},{"raw_affiliation_string":"Technological University of Pereira/ Department of Electrical Engineering, Colombia","institution_ids":["https://openalex.org/I200183955"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080328620","display_name":"G. Castellanos-Dom\u00ednguez","orcid":"https://orcid.org/0000-0002-0138-5489"},"institutions":[{"id":"https://openalex.org/I36243813","display_name":"Universidad Nacional de Colombia","ror":"https://ror.org/059yx9a68","country_code":"CO","type":"education","lineage":["https://openalex.org/I36243813"]}],"countries":["CO"],"is_corresponding":false,"raw_author_name":"C.G Castellanos-Dominguez","raw_affiliation_strings":["Universidad Nacional de Colombia Sede Manizales, Colombia","Universidad Nacional de Colombia - Sede Manizales, Colombia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad Nacional de Colombia Sede Manizales, Colombia","institution_ids":["https://openalex.org/I36243813"]},{"raw_affiliation_string":"Universidad Nacional de Colombia - Sede Manizales, Colombia","institution_ids":["https://openalex.org/I36243813"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3201,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.52797203,"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":"25","last_page":"30"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.9930999875068665,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/wavelet-packet-decomposition","display_name":"Wavelet packet decomposition","score":0.7333722114562988},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6561558246612549},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.626567006111145},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.576546847820282},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5764898061752319},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.5006799697875977},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.4912571310997009},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4747367799282074},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.46724414825439453},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4475918710231781},{"id":"https://openalex.org/keywords/energy-operator","display_name":"Energy operator","score":0.44113075733184814},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4375176727771759},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38333579897880554},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.3223228454589844},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23583564162254333}],"concepts":[{"id":"https://openalex.org/C155777637","wikidata":"https://www.wikidata.org/wiki/Q2736187","display_name":"Wavelet packet decomposition","level":4,"score":0.7333722114562988},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6561558246612549},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.626567006111145},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.576546847820282},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5764898061752319},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.5006799697875977},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.4912571310997009},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4747367799282074},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.46724414825439453},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4475918710231781},{"id":"https://openalex.org/C19579662","wikidata":"https://www.wikidata.org/wiki/Q461145","display_name":"Energy operator","level":3,"score":0.44113075733184814},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4375176727771759},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38333579897880554},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3223228454589844},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23583564162254333},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/csndsp16145.2010.5580465","is_oa":false,"landing_page_url":"https://doi.org/10.1109/csndsp16145.2010.5580465","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 7th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP 2010)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.47999998927116394,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"},{"score":0.46000000834465027,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1970732892","https://openalex.org/W1976542887","https://openalex.org/W2017960224","https://openalex.org/W2022385615","https://openalex.org/W2089332994","https://openalex.org/W2117188745","https://openalex.org/W2130740043","https://openalex.org/W2135870717","https://openalex.org/W2156447271","https://openalex.org/W2158242127","https://openalex.org/W2170117446"],"related_works":["https://openalex.org/W2380372197","https://openalex.org/W68308810","https://openalex.org/W2053682625","https://openalex.org/W1986475093","https://openalex.org/W2076239227","https://openalex.org/W2754441293","https://openalex.org/W1506615375","https://openalex.org/W2085792030","https://openalex.org/W1588899229","https://openalex.org/W1967182499"],"abstract_inverted_index":{"This":[0,212],"paper":[1],"present":[2],"a":[3,10,29,41,86,103,151,201],"novel":[4],"feature":[5],"selection":[6,247],"method":[7,180],"based":[8],"on":[9,231],"ensemble":[11,70],"of":[12,33,55,71,124,128,177],"optimal":[13,79],"wavelet":[14,35,80,97],"packet":[15,98],"and":[16,43,52,64,74,83,189,218,253],"learning":[17],"machine":[18],"(OWLM).":[19],"First,":[20],"the":[21,34,47,50,56,69,72,75,96,107,111,117,122,125,129,133,137,147,164,175,178,182,186,190,196,238,251,254,265,268],"lifting":[22],"schemes":[23,39],"(LS)":[24],"are":[25,40,58,92,144,171],"used":[26,93,172,199,223,245],"to":[27,94,160,173,224,242],"construct":[28],"single":[30],"decomposition":[31],"stage":[32],"transform":[36,81,99],"(WT),":[37],"these":[38],"fast":[42],"flexible":[44],"implementation":[45],"for":[46,105],"WT.":[48],"Then":[49,89,141],"update":[51],"predictor":[53],"operator":[54],"LS":[57,73],"optimized":[59],"by":[60,121,131,264],"Genetic":[61],"Algorithms":[62],"(GA)":[63],"Lagrange's":[65],"optimization.":[66],"So":[67],"far":[68],"GA":[76],"is":[77,85,114,119,156,198,240,262],"called":[78],"(OWT)":[82],"it":[84],"signal-adapted":[87],"decomposition.":[88],"cascaded":[90],"OWT's":[91],"implement":[95],"(WPT)":[100],"which":[101],"provides":[102],"tool":[104],"customizing":[106],"time-frequency":[108,126],"plane":[109,127],"were":[110],"signal's":[112],"energy":[113,142],"projected.":[115],"Finally":[116],"OWLM":[118,148,165,197,239,266],"constructed":[120],"optimization":[123],"WPT":[130],"maximizing":[132],"class":[134],"separability":[135],"using":[136],"Davies-Bouldin":[138],"(DB)":[139],"criterion.":[140],"features":[143,246],"computed":[145,157],"from":[146,207,228],"space,":[149],"also":[150],"principal":[152],"component":[153],"analysis":[154],"(PCA)":[155],"in":[158,163,267],"order":[159],"reduce":[161],"complexity":[162],"space.":[166],"Three":[167],"different":[168,209,219],"basic":[169],"classifiers":[170],"evaluate":[174],"performance":[176],"proposed":[179],"including":[181],"linear":[183],"Bayesian":[184,192],"classifier,":[185,188],"k-NN":[187],"quadratic":[191],"classifier.":[193],"As":[194],"example":[195],"over":[200],"microelectrode":[202],"recording":[203],"(MER)":[204],"signals":[205,213],"database":[206,269],"four":[208],"brain":[210],"zones.":[211],"have":[214,221],"strong":[215],"non-stationary":[216],"behavior":[217],"techniques":[220],"been":[222],"extract":[225],"discriminant":[226],"information":[227],"it.":[229],"Based":[230],"experiments":[232],"carried":[233],"out,":[234],"results":[235],"show":[236],"that":[237],"superior":[241],"other":[243],"commonly":[244],"methods,":[248],"such":[249],"as":[250],"WT":[252],"WPT.":[255],"The":[256],"best":[257],"MER":[258],"classification":[259],"rate":[260],"99.14%":[261],"obtained":[263],"used.":[270]},"counts_by_year":[{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}