{"id":"https://openalex.org/W2103424096","doi":"https://doi.org/10.1109/icmlc.2010.5581032","title":"Comparison of subsampling techniques for random subspace ensembles","display_name":"Comparison of subsampling techniques for random subspace ensembles","publication_year":2010,"publication_date":"2010-07-01","ids":{"openalex":"https://openalex.org/W2103424096","doi":"https://doi.org/10.1109/icmlc.2010.5581032","mag":"2103424096"},"language":"en","primary_location":{"id":"doi:10.1109/icmlc.2010.5581032","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2010.5581032","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 International Conference on Machine Learning and Cybernetics","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/A5001091340","display_name":"Santhosh Pathical","orcid":null},"institutions":[{"id":"https://openalex.org/I90871651","display_name":"University of Toledo","ror":"https://ror.org/01pbdzh19","country_code":"US","type":"education","lineage":["https://openalex.org/I90871651"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Santhosh Pathical","raw_affiliation_strings":["Electrical Engineering and Computer Science Department, University of Tolctlo and E\u00f6tv\u00f6s L\u00e1rand University, Toledo, OH, USA","Electrical Engineering and Computer Science Department, University of Toledo, Toledo, OH, 43606, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical Engineering and Computer Science Department, University of Tolctlo and E\u00f6tv\u00f6s L\u00e1rand University, Toledo, OH, USA","institution_ids":["https://openalex.org/I90871651"]},{"raw_affiliation_string":"Electrical Engineering and Computer Science Department, University of Toledo, Toledo, OH, 43606, USA","institution_ids":["https://openalex.org/I90871651"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018680547","display_name":"G\u00fcrsel Serpen","orcid":"https://orcid.org/0000-0001-9005-5483"},"institutions":[{"id":"https://openalex.org/I90871651","display_name":"University of Toledo","ror":"https://ror.org/01pbdzh19","country_code":"US","type":"education","lineage":["https://openalex.org/I90871651"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gursel Serpen","raw_affiliation_strings":["Electrical Engineering and Computer Science Department, University of Tolctlo and E\u00f6tv\u00f6s L\u00e1rand University, Toledo, OH, USA","Electrical Engineering and Computer Science Department, University of Toledo, Toledo, OH, 43606, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical Engineering and Computer Science Department, University of Tolctlo and E\u00f6tv\u00f6s L\u00e1rand University, Toledo, OH, USA","institution_ids":["https://openalex.org/I90871651"]},{"raw_affiliation_string":"Electrical Engineering and Computer Science Department, University of Toledo, Toledo, OH, 43606, USA","institution_ids":["https://openalex.org/I90871651"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I90871651"],"apc_list":null,"apc_paid":null,"fwci":0.5437,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.64094385,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"20","issue":null,"first_page":"380","last_page":"385"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9983000159263611,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9983000159263611,"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":0.9921000003814697,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9836999773979187,"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/random-subspace-method","display_name":"Random subspace method","score":0.7140800356864929},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.6975916028022766},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.6823139190673828},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6349589824676514},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6218804717063904},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.5591219663619995},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5497680902481079},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5394708514213562},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5348400473594666},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4785062372684479},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.44952359795570374},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3512345850467682},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.19585242867469788}],"concepts":[{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.7140800356864929},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.6975916028022766},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6823139190673828},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6349589824676514},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6218804717063904},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.5591219663619995},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5497680902481079},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5394708514213562},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5348400473594666},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4785062372684479},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.44952359795570374},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3512345850467682},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.19585242867469788},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icmlc.2010.5581032","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2010.5581032","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 International Conference on Machine Learning and Cybernetics","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.460.1254","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.460.1254","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.eng.utoledo.edu/~gserpen/Publications/ICMLC 2010 Manuscript.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W46831063","https://openalex.org/W61567823","https://openalex.org/W1570448133","https://openalex.org/W1578627936","https://openalex.org/W1661871015","https://openalex.org/W2023294425","https://openalex.org/W2105875820","https://openalex.org/W2109094355","https://openalex.org/W2113242816","https://openalex.org/W2128302979","https://openalex.org/W2144569038","https://openalex.org/W2150757437","https://openalex.org/W2164988966","https://openalex.org/W3015571647","https://openalex.org/W4237171445","https://openalex.org/W4252684946","https://openalex.org/W6602592809","https://openalex.org/W6636914306","https://openalex.org/W6681422789"],"related_works":["https://openalex.org/W2007593166","https://openalex.org/W1603777065","https://openalex.org/W2143234973","https://openalex.org/W2397292208","https://openalex.org/W4221136059","https://openalex.org/W1548677759","https://openalex.org/W1981866886","https://openalex.org/W109131529","https://openalex.org/W1503513760","https://openalex.org/W2052615004"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"the":[3,27,38,98,103,110],"comparison":[4],"of":[5,20,29],"three":[6,48,72],"subsampling":[7],"techniques":[8],"for":[9,44,47,102],"random":[10,21,34,54,58,63,66],"subspace":[11,22],"ensemble":[12,23,68],"classifiers":[13],"through":[14],"an":[15],"empirical":[16],"study.":[17],"A":[18],"version":[19],"designed":[24],"to":[25,108],"address":[26],"challenges":[28],"high":[30],"dimensional":[31],"classification,":[32],"entitled":[33],"subsample":[35,67],"ensemble,":[36,104],"within":[37],"voting":[39],"combiner":[40],"framework":[41],"was":[42,69],"evaluated":[43],"its":[45],"performance":[46],"different":[49,73],"sampling":[50,55,59,100,111],"methods":[51],"which":[52,105],"entailed":[53],"without":[56,112],"replacement,":[57,61],"with":[60],"and":[62,80,83],"partitioning.":[64],"The":[65],"instantiated":[70],"using":[71],"base":[74],"learners":[75],"including":[76],"C4.5,":[77],"k-nearest":[78],"neighbor,":[79],"na\u00efve":[81],"Bayes,":[82],"tested":[84],"on":[85],"five":[86],"high-dimensional":[87],"benchmark":[88],"data":[89],"sets":[90],"in":[91],"machine":[92],"learning.":[93],"Simulation":[94],"results":[95],"helped":[96],"ascertain":[97],"optimal":[99],"technique":[101],"turned":[106],"out":[107],"be":[109],"replacement.":[113]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}
