{"id":"https://openalex.org/W4400034208","doi":"https://doi.org/10.1109/eais58494.2024.10570001","title":"On the transferability of local model\u2010agnostic explanations of machine learning models to unseen data","display_name":"On the transferability of local model\u2010agnostic explanations of machine learning models to unseen data","publication_year":2024,"publication_date":"2024-05-23","ids":{"openalex":"https://openalex.org/W4400034208","doi":"https://doi.org/10.1109/eais58494.2024.10570001"},"language":"en","primary_location":{"id":"doi:10.1109/eais58494.2024.10570001","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eais58494.2024.10570001","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)","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/A5114182327","display_name":"Alba Mar\u00eda L\u00f3pez Gonz\u00e1lez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alba Mar\u00eda L\u00f3pez Gonz\u00e1lez","raw_affiliation_strings":["ETSI Informaticos, Universidad Politéecnica de Madrid,Artificial Intelligence Department,Madrid,Spain,28660"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ETSI Informaticos, Universidad Politéecnica de Madrid,Artificial Intelligence Department,Madrid,Spain,28660","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038248719","display_name":"Esteban Garc\u00eda-Cuesta","orcid":"https://orcid.org/0000-0002-1215-3333"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Esteban Garc\u00eda-Cuesta","raw_affiliation_strings":["ETSI Informaticos, Universidad Politéecnica de Madrid,Artificial Intelligence Department,Madrid,Spain,28660"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ETSI Informaticos, Universidad Politéecnica de Madrid,Artificial Intelligence Department,Madrid,Spain,28660","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.1336,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.88991909,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.923799991607666,"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":0.923799991607666,"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/transferability","display_name":"Transferability","score":0.8926004767417908},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7267332673072815},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5815103650093079},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5533344149589539},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.462685227394104},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.36969655752182007},{"id":"https://openalex.org/keywords/software-engineering","display_name":"Software engineering","score":0.05869799852371216}],"concepts":[{"id":"https://openalex.org/C61272859","wikidata":"https://www.wikidata.org/wiki/Q7834031","display_name":"Transferability","level":3,"score":0.8926004767417908},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7267332673072815},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5815103650093079},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5533344149589539},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.462685227394104},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.36969655752182007},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.05869799852371216},{"id":"https://openalex.org/C140331021","wikidata":"https://www.wikidata.org/wiki/Q1868104","display_name":"Logit","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/eais58494.2024.10570001","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eais58494.2024.10570001","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2386192529","https://openalex.org/W2516809705","https://openalex.org/W2533158557","https://openalex.org/W2950690147","https://openalex.org/W2953073956","https://openalex.org/W2970447476","https://openalex.org/W3142157246","https://openalex.org/W3147999658","https://openalex.org/W4212998595","https://openalex.org/W4232929704","https://openalex.org/W4234174474","https://openalex.org/W4282813620","https://openalex.org/W4286889204","https://openalex.org/W4289704137","https://openalex.org/W4293768783","https://openalex.org/W4311640769","https://openalex.org/W4366262984","https://openalex.org/W6752948792","https://openalex.org/W6754669440"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Numerous":[0],"methods":[1],"have":[2,38,65],"been":[3,39,45],"developed":[4],"to":[5,10,47,57,98,107],"address":[6],"the":[7,12,18,49,54,79,100,105,108,112,132,138,156,175],"critical":[8],"need":[9],"understand":[11],"behavior":[13,31],"of":[14,53,81,102,121,128,140,153],"AI":[15],"systems.":[16],"Arguably,":[17],"most":[19,62],"popular":[20],"are":[21,170],"model-agnostic":[22,84,187],"local":[23],"explanation":[24,85],"techniques,":[25],"which":[26],"focus":[27],"on":[28,67],"examining":[29],"model":[30,122],"for":[32,160,178],"individual":[33],"instances.":[34],"While":[35],"several":[36],"implementations":[37],"proposed,":[40],"comparatively":[41],"less":[42],"attention":[43],"has":[44],"paid":[46],"assessing":[48],"robustness":[50,63,80,130],"and":[51,70,88,94],"transferability":[52,101,139],"generated":[55],"explanations":[56,103,116,142],"unseen":[58],"data.":[59],"More":[60],"importantly,":[61],"analyzes":[64],"focused":[66],"differentiable":[68],"models":[69,182],"deep":[71],"neural":[72],"networks.":[73],"In":[74],"this":[75],"paper,":[76],"we":[77],"analyze":[78],"two":[82,186],"well-known":[83],"methods,":[86],"LIME":[87],"SHAP,":[89],"from":[90,104],"a":[91,96],"methodological":[92],"perspective":[93],"propose":[95],"criterion":[97],"measure":[99],"training":[106],"testing":[109],"phases.":[110],"Therefore,":[111],"proposed":[113],"methodology":[114],"validates":[115],"not":[117],"only":[118],"in":[119,126,145],"terms":[120,127],"performance":[123],"but":[124],"also":[125,166],"their":[129],"during":[131],"learning":[133,181],"process.":[134],"We":[135,165],"conclude":[136],"that":[137,152,168],"SHAP":[141],"is":[143,158],"better":[144],"sparse":[146],"or":[147],"low-density":[148],"data":[149,163],"sets":[150],"than":[151],"LIME,":[154],"while":[155],"opposite":[157],"true":[159],"very":[161],"dense":[162],"sets.":[164],"observed":[167],"there":[169],"no":[171],"significant":[172],"differences":[173],"between":[174],"results":[176],"obtained":[177],"different":[179],"machine":[180],"combined":[183],"with":[184],"these":[185],"techniques.":[188]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}