{"id":"https://openalex.org/W2270712421","doi":"https://doi.org/10.1186/s12859-016-0900-5","title":"An experimental study of the intrinsic stability of random forest variable importance measures","display_name":"An experimental study of the intrinsic stability of random forest variable importance measures","publication_year":2016,"publication_date":"2016-02-03","ids":{"openalex":"https://openalex.org/W2270712421","doi":"https://doi.org/10.1186/s12859-016-0900-5","mag":"2270712421","pmid":"https://pubmed.ncbi.nlm.nih.gov/26842629"},"language":"en","primary_location":{"id":"doi:10.1186/s12859-016-0900-5","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s12859-016-0900-5","pdf_url":"https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-016-0900-5","source":{"id":"https://openalex.org/S19032547","display_name":"BMC Bioinformatics","issn_l":"1471-2105","issn":["1471-2105"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Bioinformatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-016-0900-5","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041840086","display_name":"Huazhen Wang","orcid":"https://orcid.org/0000-0002-6548-9957"},"institutions":[{"id":"https://openalex.org/I119045251","display_name":"Huaqiao University","ror":"https://ror.org/03frdh605","country_code":"CN","type":"education","lineage":["https://openalex.org/I119045251"]},{"id":"https://openalex.org/I184558857","display_name":"Royal Holloway University of London","ror":"https://ror.org/04g2vpn86","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I184558857"]}],"countries":["CN","GB"],"is_corresponding":false,"raw_author_name":"Huazhen Wang","raw_affiliation_strings":["College of Computer Science and Technology, Huaqiao University, Jimei Avenue, Xiamen, 361021, China. wanghuazhen@hqu.edu.cn","Computer Learning Research Centre, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, UK. wanghuazhen@hqu.edu.cn","College of Computer Science and Technology, Huaqiao University, Xiamen, China","Computer Learning Research Centre, Royal Holloway, University of London, Surrey, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Huaqiao University, Jimei Avenue, Xiamen, 361021, China. wanghuazhen@hqu.edu.cn","institution_ids":["https://openalex.org/I119045251"]},{"raw_affiliation_string":"Computer Learning Research Centre, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, UK. wanghuazhen@hqu.edu.cn","institution_ids":["https://openalex.org/I184558857"]},{"raw_affiliation_string":"College of Computer Science and Technology, Huaqiao University, Xiamen, China","institution_ids":["https://openalex.org/I119045251"]},{"raw_affiliation_string":"Computer Learning Research Centre, Royal Holloway, University of London, Surrey, UK","institution_ids":["https://openalex.org/I184558857"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045825296","display_name":"Fan Yang","orcid":"https://orcid.org/0000-0002-3523-1138"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]},{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fan Yang","raw_affiliation_strings":["Automation Department, Xiamen University, Siming South Road, Xiamen, 361005, China. yang@xmu.edu.cn","Automation Department, Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Automation Department, Xiamen University, Siming South Road, Xiamen, 361005, China. yang@xmu.edu.cn","institution_ids":["https://openalex.org/I191208505","https://openalex.org/I75867142"]},{"raw_affiliation_string":"Automation Department, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070080426","display_name":"Zhiyuan Luo","orcid":"https://orcid.org/0000-0002-3336-3751"},"institutions":[{"id":"https://openalex.org/I184558857","display_name":"Royal Holloway University of London","ror":"https://ror.org/04g2vpn86","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I184558857"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Zhiyuan Luo","raw_affiliation_strings":["Computer Learning Research Centre, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, UK. zhiyuan@cs.rhul.ac.uk","Computer Learning Research Centre, Royal Holloway, University of London, Surrey, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Learning Research Centre, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, UK. zhiyuan@cs.rhul.ac.uk","institution_ids":["https://openalex.org/I184558857"]},{"raw_affiliation_string":"Computer Learning Research Centre, Royal Holloway, University of London, Surrey, UK","institution_ids":["https://openalex.org/I184558857"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5045825296"],"corresponding_institution_ids":["https://openalex.org/I191208505","https://openalex.org/I75867142"],"apc_list":{"value":1690,"currency":"GBP","value_usd":2072},"apc_paid":{"value":1690,"currency":"GBP","value_usd":2072},"fwci":9.0587,"has_fulltext":true,"cited_by_count":217,"citation_normalized_percentile":{"value":0.98104608,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"17","issue":"1","first_page":"60","last_page":"60"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.49079999327659607,"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"}},"topics":[{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.49079999327659607,"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"}},{"id":"https://openalex.org/T11297","display_name":"Ferroptosis and cancer prognosis","score":0.03929999843239784,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11413","display_name":"Risk and Portfolio Optimization","score":0.03420000150799751,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.6906185746192932},{"id":"https://openalex.org/keywords/randomness","display_name":"Randomness","score":0.6822016835212708},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.620472252368927},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5777664184570312},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5410285592079163},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.508243978023529},{"id":"https://openalex.org/keywords/spearmans-rank-correlation-coefficient","display_name":"Spearman's rank correlation coefficient","score":0.5024263858795166},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.47585126757621765},{"id":"https://openalex.org/keywords/monotonic-function","display_name":"Monotonic function","score":0.47193998098373413},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.47126007080078125},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4395321309566498},{"id":"https://openalex.org/keywords/pearson-product-moment-correlation-coefficient","display_name":"Pearson product-moment correlation coefficient","score":0.4354560375213623},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.36422020196914673},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3588430881500244},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23553520441055298},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2026059627532959},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.13474687933921814}],"concepts":[{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.6906185746192932},{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.6822016835212708},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.620472252368927},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5777664184570312},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5410285592079163},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.508243978023529},{"id":"https://openalex.org/C159744936","wikidata":"https://www.wikidata.org/wiki/Q1126730","display_name":"Spearman's rank correlation coefficient","level":2,"score":0.5024263858795166},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.47585126757621765},{"id":"https://openalex.org/C72169020","wikidata":"https://www.wikidata.org/wiki/Q194404","display_name":"Monotonic function","level":2,"score":0.47193998098373413},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.47126007080078125},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4395321309566498},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.4354560375213623},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.36422020196914673},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3588430881500244},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23553520441055298},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2026059627532959},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.13474687933921814},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D005787","descriptor_name":"Gene Frequency","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005787","descriptor_name":"Gene Frequency","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005787","descriptor_name":"Gene Frequency","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008957","descriptor_name":"Models, Genetic","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008957","descriptor_name":"Models, Genetic","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008957","descriptor_name":"Models, Genetic","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012984","descriptor_name":"Software","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012984","descriptor_name":"Software","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012984","descriptor_name":"Software","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016000","descriptor_name":"Cluster Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016000","descriptor_name":"Cluster Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016000","descriptor_name":"Cluster Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D019295","descriptor_name":"Computational Biology","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D019295","descriptor_name":"Computational Biology","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D019295","descriptor_name":"Computational Biology","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D020641","descriptor_name":"Polymorphism, Single Nucleotide","qualifier_ui":"Q000235","qualifier_name":"genetics","is_major_topic":false},{"descriptor_ui":"D020641","descriptor_name":"Polymorphism, Single Nucleotide","qualifier_ui":"Q000235","qualifier_name":"genetics","is_major_topic":false},{"descriptor_ui":"D020641","descriptor_name":"Polymorphism, Single Nucleotide","qualifier_ui":"Q000235","qualifier_name":"genetics","is_major_topic":false},{"descriptor_ui":"D020869","descriptor_name":"Gene Expression Profiling","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D020869","descriptor_name":"Gene Expression Profiling","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D020869","descriptor_name":"Gene Expression Profiling","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false}],"locations_count":3,"locations":[{"id":"doi:10.1186/s12859-016-0900-5","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s12859-016-0900-5","pdf_url":"https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-016-0900-5","source":{"id":"https://openalex.org/S19032547","display_name":"BMC Bioinformatics","issn_l":"1471-2105","issn":["1471-2105"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Bioinformatics","raw_type":"journal-article"},{"id":"pmid:26842629","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/26842629","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC bioinformatics","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:4739337","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/4739337","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BMC Bioinformatics","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1186/s12859-016-0900-5","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s12859-016-0900-5","pdf_url":"https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-016-0900-5","source":{"id":"https://openalex.org/S19032547","display_name":"BMC Bioinformatics","issn_l":"1471-2105","issn":["1471-2105"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Bioinformatics","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Life in Land","score":0.7400000095367432,"id":"https://metadata.un.org/sdg/15"}],"awards":[{"id":"https://openalex.org/G3040833130","display_name":null,"funder_award_id":"61202144","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321878","display_name":"Natural Science Foundation of Fujian Province","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2270712421.pdf","grobid_xml":"https://content.openalex.org/works/W2270712421.grobid-xml"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W74439177","https://openalex.org/W144440563","https://openalex.org/W217432794","https://openalex.org/W1510810196","https://openalex.org/W1520812622","https://openalex.org/W1547400538","https://openalex.org/W1966375248","https://openalex.org/W1973714356","https://openalex.org/W1985372952","https://openalex.org/W1993509040","https://openalex.org/W2009374545","https://openalex.org/W2013121895","https://openalex.org/W2027739797","https://openalex.org/W2027752285","https://openalex.org/W2032909675","https://openalex.org/W2034489756","https://openalex.org/W2038894244","https://openalex.org/W2049129660","https://openalex.org/W2050431484","https://openalex.org/W2059333296","https://openalex.org/W2059989526","https://openalex.org/W2065427498","https://openalex.org/W2072623705","https://openalex.org/W2087025987","https://openalex.org/W2096149767","https://openalex.org/W2102636708","https://openalex.org/W2107376270","https://openalex.org/W2124005542","https://openalex.org/W2131822674","https://openalex.org/W2140372599","https://openalex.org/W2141270567","https://openalex.org/W2143426320","https://openalex.org/W2144327682","https://openalex.org/W2146739527","https://openalex.org/W2150904363","https://openalex.org/W2151990594","https://openalex.org/W2162162988","https://openalex.org/W2163246541","https://openalex.org/W2165432359","https://openalex.org/W2167561997","https://openalex.org/W2593086047","https://openalex.org/W2779609889","https://openalex.org/W2911964244","https://openalex.org/W2950851375","https://openalex.org/W4239110337","https://openalex.org/W4256046779","https://openalex.org/W4301629519","https://openalex.org/W4380303417"],"related_works":["https://openalex.org/W1911095394","https://openalex.org/W2749680699","https://openalex.org/W2146096861","https://openalex.org/W2773390159","https://openalex.org/W2999441357","https://openalex.org/W1478087083","https://openalex.org/W3152154711","https://openalex.org/W2963320501","https://openalex.org/W1996619012","https://openalex.org/W4232364410"],"abstract_inverted_index":{"BACKGROUND:":[0],"The":[1,101,166,228],"stability":[2,23,61,116,193,205,295,304,319],"of":[3,24,59,62,76,103,114,117,128,147,191,194,213,218,269,277,283,302,310,317,320,326,344,352,361,369,377],"Variable":[4],"Importance":[5],"Measures":[6],"(VIMs)":[7],"based":[8],"on":[9,21,171,200,224,388],"random":[10,278],"forest":[11],"has":[12,248],"recently":[13],"received":[14],"increased":[15],"attention.":[16],"Despite":[17],"the":[18,36,68,112,126,144,162,189,201,225,242,300,315,324,341,359,366,378],"extensive":[19],"attention":[20],"traditional":[22,311],"data":[25,79,135,332],"perturbations":[26,80,136,333],"or":[27,137,334],"parameter":[28,82,138,275,335],"variations,":[29,139,336],"few":[30],"studies":[31],"include":[32],"influences":[33],"coming":[34],"from":[35,134,143,266,331,340],"intrinsic":[37,60,115,145,163,192,204,226,243,252,267,294,303,318,342,370],"randomness":[38,146,343],"in":[39,51,73],"generating":[40],"VIMs,":[41,63,87,386],"i.e.":[42],"bagging,":[43],"randomization":[44],"and":[45,81,93,152,181,197,206,215,237,259,296,355,380,391],"permutation.":[46],"To":[47],"address":[48],"these":[49],"influences,":[50],"this":[52,104],"paper":[53],"we":[54,109,155],"introduce":[55],"a":[56,221,280,349],"new":[57],"concept":[58],"which":[64],"is":[65,106,285,305],"defined":[66],"as":[67],"self-consistence":[69],"among":[70],"feature":[71],"rankings":[72],"repeated":[74],"runs":[75],"VIMs":[77,118,129,321,327],"without":[78],"variations.":[83],"Two":[84],"widely":[85],"used":[86],"i.e.,":[88],"Mean":[89,94],"Decrease":[90,95],"Accuracy":[91],"(MDA)":[92],"Gini":[96],"(MDG)":[97],"are":[98,168],"comprehensively":[99,156],"investigated.":[100],"motivation":[102],"study":[105],"two-fold.":[107],"First,":[108,314],"empirically":[110],"verify":[111],"prevalence":[113,190,316],"over":[119],"many":[120],"real-world":[121],"datasets":[122,174,262],"to":[123,274],"highlight":[124],"that":[125,210,256,309,323],"instability":[127,268,325,360],"does":[130],"not":[131,328],"originate":[132],"exclusively":[133],"but":[140,337],"also":[141,338],"stems":[142,339],"VIMs.":[148,195,270,345,362],"Second,":[149,363],"through":[150],"Spearman":[151,196],"Pearson":[153,198],"tests":[154,199],"investigate":[157],"how":[158],"different":[159,207],"factors":[160,208,368],"influence":[161],"stability.":[164,227,253,312],"RESULTS:":[165],"experiments":[167],"carried":[169],"out":[170],"19":[172],"benchmark":[173],"with":[175,241,251,272],"diverse":[176],"characteristics,":[177],"including":[178],"10":[179],"high-dimensional":[180],"small-sample":[182,258,390],"gene":[183],"expression":[184],"datasets.":[185,394],"Experimental":[186],"results":[187],"demonstrate":[188],"correlations":[202,250,289],"between":[203,293],"show":[209],"#feature":[211],"(number":[212],"features)":[214],"#sample":[216],"(size":[217],"sample)":[219],"have":[220],"coupling":[222],"effect":[223],"synthetic":[229],"indictor,":[230],"#feature/#sample,":[231],"shows":[232],"both":[233],"negative":[234,238],"monotonic":[235,249],"correlation":[236,240],"linear":[239],"stability,":[244,354,371],"while":[245],"OOB":[246],"accuracy":[247],"This":[254,346],"indicates":[255],"high-dimensional,":[257,389],"high":[260,392],"complexity":[261,393],"may":[263,356],"suffer":[264],"more":[265,375,382],"Furthermore,":[271],"respect":[273],"settings":[276],"forest,":[279],"large":[281],"number":[282],"trees":[284],"preferred.":[286],"No":[287],"significant":[288],"can":[290],"be":[291,374],"seen":[292],"other":[297],"factors.":[298],"Finally,":[299],"magnitude":[301],"always":[306],"smaller":[307],"than":[308],"CONCLUSION:":[313],"demonstrates":[322],"only":[329],"comes":[330],"finding":[347],"gives":[348],"better":[350],"understanding":[351],"VIM":[353],"help":[357],"reduce":[358],"by":[364],"investigating":[365],"potential":[367],"users":[372],"would":[373],"aware":[376],"risks":[379],"hence":[381],"careful":[383],"when":[384],"using":[385],"especially":[387]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":37},{"year":2024,"cited_by_count":43},{"year":2023,"cited_by_count":38},{"year":2022,"cited_by_count":33},{"year":2021,"cited_by_count":23},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":12},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":3}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
