{"id":"https://openalex.org/W2909288227","doi":"https://doi.org/10.1109/phm-chongqing.2018.00224","title":"Analyzing Data Complexity Using Metafeatures for Classification Algorithm Selection","display_name":"Analyzing Data Complexity Using Metafeatures for Classification Algorithm Selection","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2909288227","doi":"https://doi.org/10.1109/phm-chongqing.2018.00224","mag":"2909288227"},"language":"en","primary_location":{"id":"doi:10.1109/phm-chongqing.2018.00224","doi":"https://doi.org/10.1109/phm-chongqing.2018.00224","is_oa":false,"landing_page_url":"https://doi.org/10.1109/phm-chongqing.2018.00224","pdf_url":null,"source":{"id":"https://openalex.org/S4306420743","display_name":"Prognostics and System Health Management Conference","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Prognostics and System Health Management Conference (PHM-Chongqing)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5073986255","display_name":"Rushit Shah","orcid":"https://orcid.org/0000-0001-8670-1454","observed_orcids":["https://orcid.org/0000-0001-8670-1454"]},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rushit Shah","raw_affiliation_strings":["Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070499287","display_name":"Varun Khemani","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Varun Khemani","raw_affiliation_strings":["Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000848090","display_name":"Michael H. Azarian","orcid":"https://orcid.org/0000-0002-1434-6972","observed_orcids":["https://orcid.org/0000-0002-1434-6972"]},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael Azarian","raw_affiliation_strings":["Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013287421","display_name":"MICHAEL G. PECHT","orcid":"https://orcid.org/0000-0003-1126-8662","observed_orcids":["https://orcid.org/0000-0003-1126-8662"]},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael Pecht","raw_affiliation_strings":["Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004986172","display_name":"Yan Su","orcid":"https://orcid.org/0000-0003-4368-4265","observed_orcids":["https://orcid.org/0000-0003-4368-4265"]},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Su","raw_affiliation_strings":["Civil Aviation College, Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Civil Aviation College, Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0885,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.26778629,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":95},"biblio":{"volume":"10","issue":null,"first_page":"1280","last_page":"1284"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9940999746322632,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9940999746322632,"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/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.0017000000225380063,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12761","display_name":"Data Stream Mining Techniques","score":0.0003000000142492354,"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/algorithm-selection","display_name":"algorithm selection","score":0.6589999794960022},{"id":"https://openalex.org/keywords/classification","display_name":"classification","score":0.6420000195503235},{"id":"https://openalex.org/keywords/meta-learning","display_name":"meta-learning","score":0.7590000033378601},{"id":"https://openalex.org/keywords/data-complexity","display_name":"data complexity","score":0.8259999752044678},{"id":"https://openalex.org/keywords/classification-performance","display_name":"classification performance","score":0.6019999980926514},{"id":"https://openalex.org/keywords/performance-maps","display_name":"performance maps","score":0.4699999988079071},{"id":"https://openalex.org/keywords/ensemble-classifiers","display_name":"ensemble classifiers","score":0.40299999713897705}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7602800726890564},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7002420425415039},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6649336814880371},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.641795814037323},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.5920611619949341},{"id":"https://openalex.org/C139532973","wikidata":"https://www.wikidata.org/wiki/Q2679259","display_name":"Linear classifier","level":3,"score":0.5701039433479309},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.5273431539535522},{"id":"https://openalex.org/C34872919","wikidata":"https://www.wikidata.org/wiki/Q7092302","display_name":"One-class classification","level":3,"score":0.4269797205924988},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4209403395652771},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12388664484024048},{"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/phm-chongqing.2018.00224","doi":"https://doi.org/10.1109/phm-chongqing.2018.00224","is_oa":false,"landing_page_url":"https://doi.org/10.1109/phm-chongqing.2018.00224","pdf_url":null,"source":{"id":"https://openalex.org/S4306420743","display_name":"Prognostics and System Health Management Conference","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Prognostics and System Health Management Conference (PHM-Chongqing)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W60739440","https://openalex.org/W1873332500","https://openalex.org/W1967320885","https://openalex.org/W2001619934","https://openalex.org/W2027717478","https://openalex.org/W2090136295","https://openalex.org/W2125877832","https://openalex.org/W2169384781","https://openalex.org/W2488735262","https://openalex.org/W2573242108","https://openalex.org/W3145506661","https://openalex.org/W4206070770","https://openalex.org/W4246649397","https://openalex.org/W4256561644"],"related_works":["https://openalex.org/W3091590903","https://openalex.org/W2129131223","https://openalex.org/W4414323469","https://openalex.org/W7039470118","https://openalex.org/W2914983086","https://openalex.org/W2902777483","https://openalex.org/W7160289210","https://openalex.org/W3113997496","https://openalex.org/W2992838583","https://openalex.org/W2999917162"],"abstract_inverted_index":{"Supervised":[0],"machine":[1],"learning":[2],"is":[3,42,123,142,149,276],"defined":[4,44],"as":[5,45,240,312],"the":[6,34,46,61,77,95,112,116,128,133,145,187,214,222,247,268,297,301,305,313,322,333],"task":[7,122,185,215],"of":[8,39,48,57,64,135,153,216,234,236,250,260,271,321,326],"seeking":[9],"algorithms":[10],"which,":[11],"based":[12],"on":[13,246],"reasoning":[14],"gained":[15],"from":[16,160],"historical":[17],"data,":[18],"produce":[19],"a":[20,54,82,99,182,196,227,232,258,261,289,336],"general":[21],"hypothesis,":[22],"can":[23,293,309],"then":[24],"be":[25,265,294,310],"used":[26,253],"to":[27,50,80,180,212,229,264],"infer":[28],"knowledge":[29],"about":[30],"new":[31,55,83,137,290,306,337],"data":[32,58,84,101,158,171,177,237,272,291,307,338],"in":[33,296,318],"future.":[35],"Classification,":[36],"an":[37,88],"instance":[38],"supervised":[40],"learning,":[41],"statistically":[43],"problem":[47,141,325],"identifying":[49],"which":[51,114,198],"particular":[52],"subpopulation":[53],"observation":[56],"belongs.":[59],"Given":[60],"large":[62],"number":[63],"available":[65],"classification":[66,118,155,184,202,220,248],"algorithms,":[67],"their":[68,244],"combinations":[69],"(e.g.":[70],"ensembles)":[71],"and":[72,110,127,242,300],"possible":[73],"parameter":[74],"settings,":[75],"finding":[76,94],"best":[78,96,117,317],"method":[79],"analyze":[81,230],"set":[85,102,233,292,308],"has":[86],"become":[87],"ever":[89],"more":[90],"challenging":[91],"task.":[92],"Typically,":[93],"classifier":[97,165,197,303],"for":[98,175,219,304,335],"given":[100],"involves":[103,330],"empirically":[104,231],"iterating":[105],"through":[106],"all":[107],"candidate":[108,138],"classifiers":[109,282],"choosing":[111],"one":[113,193,314],"provides":[115],"accuracies.":[119],"Clearly,":[120],"this":[121,207,209],"computationally":[124],"very":[125],"expensive":[126],"computation":[129],"cost":[130],"increases":[131],"with":[132,168],"addition":[134],"each":[136],"algorithm.":[139],"This":[140,274],"compounded":[143],"by":[144],"fact":[146],"that":[147,315,319],"there":[148],"not":[150,173],"adequate":[151],"generalizability":[152],"these":[154],"methods":[156],"over":[157,267],"sets":[159],"different":[161],"domains.":[162],"For":[163],"example,":[164],"performance":[166,203,249,262],"obtained":[167],"medical":[169],"imaging":[170],"may":[172],"hold":[174],"financial":[176],"when":[178],"trying":[179],"achieve":[181],"similar":[183],"using":[186,221],"same":[188],"classifier.":[189],"How,":[190],"then,":[191,329],"does":[192],"efficiently":[194],"choose":[195],"will":[199],"provide":[200],"better":[201,284],"than":[204,285],"others?":[205],"In":[206],"context,":[208],"study":[210],"aims":[211],"streamline":[213],"algorithm":[217,327],"selection":[218],"meta-learning":[223],"framework.":[224],"We":[225],"propose":[226],"methodology":[228],"measures":[235],"complexity,":[238],"known":[239],"metafeatures,":[241],"investigate":[243],"influence":[245],"several":[251],"widely":[252],"classifiers.":[254],"Doing":[255],"so":[256],"allows":[257],"map":[259,275],"metric":[263],"generated":[266],"metafeature":[269,298],"space":[270],"sets.":[273],"partitioned":[277],"into":[278],"regions":[279],"where":[280],"some":[281],"perform":[283],"others.":[286],"Once":[287],"implemented,":[288],"located":[295],"continuum":[299],"appropriate":[302],"chosen":[311],"performs":[316],"region":[320],"map.":[323],"The":[324],"selection,":[328],"merely":[331],"calculating":[332],"metafeatures":[334],"set.":[339]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2019-01-25T00:00:00"}