{"id":"https://openalex.org/W7163922358","doi":"https://doi.org/10.48550/arxiv.2606.07068","title":"Bias in Filter Feature Selection Evaluation: A Meta-Analysis of Datasets, Baselines, and Experimental Design Choices","display_name":"Bias in Filter Feature Selection Evaluation: A Meta-Analysis of Datasets, Baselines, and Experimental Design Choices","publication_year":2026,"publication_date":"2026-06-05","ids":{"openalex":"https://openalex.org/W7163922358","doi":"https://doi.org/10.48550/arxiv.2606.07068"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.07068","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07068","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.07068","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092869667","display_name":"Malick Ebiele","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ebiele, Malick","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138190997","display_name":"Malika Bendechache","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bendechache, Malika","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5087699589","display_name":"Rob Brennan","orcid":"https://orcid.org/0000-0001-8236-362X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Brennan, Rob","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.5443999767303467,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.5443999767303467,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.07680000364780426,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.06530000269412994,"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/feature-selection","display_name":"Feature selection","score":0.7325999736785889},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4936999976634979},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4936999976634979},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4763999879360199},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.43970000743865967},{"id":"https://openalex.org/keywords/selection-bias","display_name":"Selection bias","score":0.4375999867916107},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.43369999527931213},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.3935000002384186}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7325999736785889},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6535999774932861},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5827999711036682},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5404000282287598},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4936999976634979},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4936999976634979},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4763999879360199},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.43970000743865967},{"id":"https://openalex.org/C40423286","wikidata":"https://www.wikidata.org/wiki/Q284172","display_name":"Selection bias","level":2,"score":0.4375999867916107},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.43369999527931213},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3935000002384186},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.391400009393692},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37220001220703125},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3702000081539154},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.3549000024795532},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.3240000009536743},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.2994000017642975},{"id":"https://openalex.org/C44648626","wikidata":"https://www.wikidata.org/wiki/Q1049848","display_name":"Percentage point","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C186027771","wikidata":"https://www.wikidata.org/wiki/Q4008379","display_name":"Valuation (finance)","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.2815000116825104},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.27079999446868896}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.07068","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07068","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.07068","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07068","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"Background:":[0],"Since":[1],"1990":[2],"many":[3],"feature":[4,38,84,90,257],"selection":[5,39,85,91,258],"methods":[6,191,215],"have":[7],"been":[8],"proposed":[9],"across":[10],"heterogeneous":[11],"applications.":[12],"To":[13],"validate":[14],"the":[15,33,62,109,158,184,187,200,205,211,230,241,253,256,260,267,270,276],"usefulness":[16],"of":[17,64,95,133,177,183,189,202,207,213,255,262,269],"a":[18,37,79,131,175],"new":[19,65,190,214],"method,":[20],"it":[21],"needs":[22],"to":[23,100,104,121,150,240,274],"be":[24,71],"compared":[25],"against":[26,192],"at":[27,42],"least":[28,43],"one":[29,44],"baseline":[30],"method":[31],"from":[32],"existing":[34],"literature":[35],"on":[36,148],"task":[40],"using":[41],"dataset.":[45],"Recent":[46],"developments":[47],"in":[48,56,83,88,119,186],"tabular":[49],"Deep":[50],"Learning":[51,58],"(DL)":[52],"and":[53,68,111,142,160,210,264,266],"data":[54],"valuation":[55],"Machine":[57],"(ML)":[59],"suggest":[60],"that":[61,78,107,112,181,227,243],"evaluation":[63,110],"methods,":[66],"algorithms,":[67],"models":[69],"may":[70],"consciously":[72],"or":[73],"unconsciously":[74],"biased.":[75],"We":[76,129],"hypothesise":[77],"similar":[80],"trend":[81],"exists":[82],"(FS),":[86],"particularly":[87],"filter":[89],"(FFS).":[92],"The":[93,144,234],"aim":[94],"this":[96,228],"study":[97],"is":[98,197,219,224,229,238,246],"therefore":[99],"examine":[101,151],"FFS":[102,126,137,152,167],"studies":[103,138],"identify":[105],"factors":[106,250],"influence":[108],"might":[113],"consist":[114],"entry":[115],"point":[116],"for":[117,125,165],"biases":[118],"order":[120],"recommend":[122],"stronger":[123],"principles":[124],"evaluation.":[127,168],"Methods:":[128],"analyse":[130],"sample":[132],"28":[134],"high":[135],"profile":[136],"published":[139],"between":[140],"1994":[141],"2025.":[143],"analysis":[145,173],"provides":[146],"reflections":[147],"how":[149],"studies,":[153],"highlights":[154],"lessons":[155],"learned":[156],"throughout":[157],"process,":[159],"gives":[161],"five":[162],"evidence-based":[163],"recommendations":[164],"future":[166],"Results:":[169],"Multivariate":[170],"Linear":[171],"Regression":[172],"achieved":[174],"score":[176],"$R^2=0.33$.":[178],"It":[179],"means":[180],"33%":[182],"variance":[185],"performance":[188],"chosen":[193],"baselines":[194,208],"(win":[195],"rate)":[196],"explained":[198],"by":[199,248],"number":[201,206,212],"datasets":[203,263],"(#Datasets),":[204],"(#Baselines),":[209],"(#NewMethods).":[216],"Discussion:":[217],"$R^2=0.33$":[218],"considered":[220],"medium":[221,235],"explanation;":[222],"which":[223],"promising":[225],"given":[226],"first":[231],"such":[232,251],"study.":[233],"explanation":[236],"result":[237],"due":[239],"fact":[242],"win":[244],"rate":[245],"influenced":[247],"additional":[249],"as":[252],"maturity":[254],"domain,":[259],"type":[261],"baselines,":[265],"simplicity":[268],"regression":[271],"model":[272],"used":[273],"explain":[275],"relationship.":[277]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-09T00:00:00"}
