{"id":"https://openalex.org/W6962789948","doi":"https://doi.org/10.17877/de290r-21906","title":"Integration of feature selection stability in model fitting","display_name":"Integration of feature selection stability in model fitting","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W6962789948","doi":"https://doi.org/10.17877/de290r-21906"},"language":"en","primary_location":{"id":"doi:10.17877/de290r-21906","is_oa":true,"landing_page_url":"https://doi.org/10.17877/de290r-21906","pdf_url":null,"source":{"id":"https://openalex.org/S4306400811","display_name":"Technische Universit\u00e4t Dortmund Eldorado (Technische Universit\u00e4t Dortmund)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210148506","host_organization_name":"Erich-Brost-Institut","host_organization_lineage":["https://openalex.org/I4210148506"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.17877/de290r-21906","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Bommert, Andrea Martina","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bommert, Andrea Martina","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.83973854,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.75267878,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.45320001244544983,"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":0.45320001244544983,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.04540000110864639,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.03720000013709068,"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/stability","display_name":"Stability (learning theory)","score":0.7452999949455261},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.6845999956130981},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6747000217437744},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6660000085830688},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4968000054359436},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4408999979496002},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.3714999854564667}],"concepts":[{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.7452999949455261},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.6845999956130981},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6747000217437744},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6660000085830688},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4968000054359436},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.46050000190734863},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4408999979496002},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4250999987125397},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41110000014305115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40869998931884766},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.3714999854564667},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3287000060081482},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30550000071525574},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.28459998965263367},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C102248274","wikidata":"https://www.wikidata.org/wiki/Q168388","display_name":"Adaptive filter","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.17877/de290r-21906","is_oa":true,"landing_page_url":"https://doi.org/10.17877/de290r-21906","pdf_url":null,"source":{"id":"https://openalex.org/S4306400811","display_name":"Technische Universit\u00e4t Dortmund Eldorado (Technische Universit\u00e4t Dortmund)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210148506","host_organization_name":"Erich-Brost-Institut","host_organization_lineage":["https://openalex.org/I4210148506"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.17877/de290r-21906","is_oa":true,"landing_page_url":"https://doi.org/10.17877/de290r-21906","pdf_url":null,"source":{"id":"https://openalex.org/S4306400811","display_name":"Technische Universit\u00e4t Dortmund Eldorado (Technische Universit\u00e4t Dortmund)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210148506","host_organization_name":"Erich-Brost-Institut","host_organization_lineage":["https://openalex.org/I4210148506"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0,270,456],"this":[1,271],"thesis,":[2,272],"four":[3],"aspects":[4],"connected":[5],"to":[6,55,104,116,296,315,333,364,392,453,458],"feature":[7,18,28,58,198,279,397],"selection":[8,19,29,59,280,323,398],"are":[9,31,42,94,100,144,169,185,204,230,247,288,346],"analyzed:":[10],"Firstly,":[11],"a":[12,46,137,252,307,320,387,412],"benchmark":[13,87],"of":[14,27,38,88,122,130,182,191,209,212,275,285,324,378],"filter":[15,89,92,98,124,132,150],"methods":[16,93,99,133,151],"for":[17,24,49,70,74,163,225,251,485],"is":[20,63,81,219,259,304,313,424,439,483],"conducted.":[21],"Secondly,":[22],"measures":[23,41,162,168,184,193,229,240],"the":[25,39,86,97,105,111,127,131,141,183,196,207,210,216,222,227,256,261,266,273,278,283,301,338,350,355,376,403,442,446,459,462,468,480],"assessment":[26],"stability":[30,40,164,167,192,217,228,239,281,404,408,413],"compared":[32,101,170,332,452],"both":[33,173,393,428],"theoretically":[34],"and":[35,61,65,83,113,140,176,206,282,382,396,430],"empirically.":[36],"Some":[37],"newly":[43,186],"defined.":[44],"Thirdly,":[45],"multi-criteria":[47,308,343,388],"approach":[48,69,377,423,444,464,482],"obtaining":[50],"desirable":[51,72,292],"models":[52,73,345,356,487],"with":[53,77,102,114,136,294,342,358,362,370,390,405,488],"respect":[54,103,115,295,363,391],"predictive":[56,128,138,254,257,330,339,365,394,450,493],"accuracy,":[57],"stability,":[60],"sparsity":[62],"proposed":[64,187,443,463,481],"evaluated.":[66,84],"Fourthly,":[67],"an":[68,406],"finding":[71,268],"data":[75,157,352,368,432],"sets":[76,199,369],"many":[78,156,371],"similar":[79,123,372],"features":[80,112,214,287,326,470,490],"suggested":[82],"For":[85,367],"methods,":[90],"20":[91,166],"analyzed.":[95,231],"First,":[96],"order":[106],"in":[107,147,244,265,306,386],"which":[108,238],"they":[109],"rank":[110],"their":[117,178],"scaling":[118],"behavior,":[119],"identifying":[120],"groups":[121],"methods.":[125],"Next,":[126],"accuracy":[129,258,331,395],"when":[134],"combined":[135],"model":[139,267,334],"run":[142,223],"time":[143],"analyzed,":[145],"resulting":[146],"recommendations":[148,236],"on":[149,155,172,177,215,233,237,427,435],"that":[152,194,318,348,410,415,441],"work":[153],"well":[154],"sets.":[158,433],"To":[159,290],"identify":[160],"suitable":[161],"assessment,":[165],"based":[171,426],"theoretical":[174],"properties":[175],"empirical":[179],"behavior.":[180],"Five":[181],"by":[188],"us.":[189],"Groups":[190],"consider":[195],"same":[197,447],"as":[200],"stable":[201,322],"or":[202,448,474],"unstable":[203],"identified":[205],"impact":[208],"number":[211,284],"selected":[213,286],"values":[218],"studied.":[220],"Additionally,":[221],"times":[224],"calculating":[226],"Based":[232,434],"all":[234,297],"analyses,":[235],"should":[241],"be":[242],"used":[243],"future":[245],"analyses":[246],"made.":[248],"When":[249],"searching":[250],"good":[253],"model,":[255],"usually":[260],"only":[262,336,361],"criterion":[263],"considered":[264],"process.":[269],"benefits":[274],"additionally":[276],"considering":[277,337],"investigated.":[289],"find":[291,316],"configurations":[293,317],"three":[298],"performance":[299,451],"criteria,":[300],"hyperparameter":[302,385],"tuning":[303,360,383],"performed":[305],"fashion.":[309],"This":[310],"way,":[311],"it":[312,438],"possible":[314],"perform":[319],"more":[321],"fewer":[325,489],"without":[327,491],"losing":[328,492],"much":[329],"fitting":[335,486],"performance.":[340],"Also,":[341],"tuning,":[344],"obtained":[347,357],"over-fit":[349],"training":[351],"less":[353],"than":[354],"single-criteria":[359],"accuracy.":[366,494],"features,":[373],"we":[374],"propose":[375],"employing":[379],"L0-regularized":[380],"regression":[381],"its":[384],"fashion":[389],"stability.":[399],"We":[400],"suggest":[401],"assessing":[402],"adjusted":[407],"measure,":[409],"is,":[411],"measure":[414],"takes":[416],"into":[417],"account":[418],"similarities":[419],"between":[420],"features.":[421,476],"The":[422],"evaluated":[425],"simulated":[429,436],"real":[431,478],"data,":[437,479],"observed":[440],"achieves":[445],"better":[449],"established":[454],"approaches.":[455],"contrast":[457],"competing":[460],"approaches,":[461],"succeeds":[465],"at":[466],"selecting":[467],"relevant":[469],"while":[471],"avoiding":[472],"irrelevant":[473],"redundant":[475],"On":[477],"beneficial":[484]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}