{"id":"https://openalex.org/W4416250593","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227801","title":"Neural Network Interpretability and Incremental Correction Based on Conceptual Spatial Relationships","display_name":"Neural Network Interpretability and Incremental Correction Based on Conceptual Spatial Relationships","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250593","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227801"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227801","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227801","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","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/A5086184393","display_name":"Lijun Gao","orcid":"https://orcid.org/0000-0003-3350-0362"},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lijun Gao","raw_affiliation_strings":["Shenyang Aerospace University,School of Computer Science,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University,School of Computer Science,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043617790","display_name":"Linchao Zhu","orcid":"https://orcid.org/0000-0002-4093-7557"},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Luli Zhu","raw_affiliation_strings":["Shenyang Aerospace University,School of Computer Science,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University,School of Computer Science,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5086184393"],"corresponding_institution_ids":["https://openalex.org/I125904092"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19420548,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9886999726295471,"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":0.9886999726295471,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.002300000051036477,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0007999999797903001,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.974399983882904},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.583899974822998},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5594000220298767},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.4309999942779541},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35839998722076416},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.3149999976158142}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.974399983882904},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.739300012588501},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6140999794006348},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.583899974822998},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5594000220298767},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5593000054359436},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.4309999942779541},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35839998722076416},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3400000035762787},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.31619998812675476},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3149999976158142},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C2780233690","wikidata":"https://www.wikidata.org/wiki/Q535347","display_name":"Transparency (behavior)","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.28619998693466187},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.27410000562667847},{"id":"https://openalex.org/C48164120","wikidata":"https://www.wikidata.org/wiki/Q4491893","display_name":"Concept learning","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227801","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227801","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2295107390","https://openalex.org/W2788403449","https://openalex.org/W2791091755","https://openalex.org/W2798626461","https://openalex.org/W2962858109","https://openalex.org/W2963125461","https://openalex.org/W3006064320","https://openalex.org/W3013325675","https://openalex.org/W3034697737","https://openalex.org/W3035253074","https://openalex.org/W3127493833","https://openalex.org/W3157574776","https://openalex.org/W3194668998","https://openalex.org/W4310381212"],"related_works":[],"abstract_inverted_index":{"Although":[0],"neural":[1,63],"networks":[2,64,145],"are":[3,44,74,122,146],"widely":[4],"used":[5,147],"in":[6,177,204],"various":[7],"fields":[8],"and":[9,18,37,67,81,107,150,169,201,208],"have":[10],"made":[11],"substantial":[12],"progress,":[13],"they":[14],"still":[15],"lack":[16],"transparency":[17],"interpretability":[19,25],"to":[20,46,57,90,117,129],"a":[21,53],"large":[22],"extent.":[23],"Recently,":[24],"methods":[26,43],"explain":[27],"model":[28,87],"decisions":[29,60],"by":[30,96,159,196],"visualizing":[31],"the":[32,38,59,85,92,97,99,102,108,112,120,131,151,154,160,163,166,170,174,178,188,194,205],"correlation":[33,135],"between":[34,101,162],"input":[35],"pixels":[36],"final":[39,86],"output,":[40],"but":[41],"these":[42],"limited":[45],"explaining":[47],"low-level":[48],"relationships.":[49],"Firstly,":[50],"we":[51],"propose":[52],"concept-based":[54],"explanation":[55],"method":[56,192],"interpret":[58],"of":[61,104,133,153,165,173],"classification":[62],"using":[65,76],"intuitive":[66],"structured":[68],"visual":[69],"concepts.":[70,127],"The":[71,181],"extracted":[72],"concepts":[73,103,109,164,172],"organized":[75],"concept":[77,179],"maps,":[78],"providing":[79],"logical":[80],"concept-level":[82],"explanations":[83],"for":[84,148],"decisions.":[88],"Secondly,":[89],"address":[91,130],"false":[93,134],"correlations":[94],"learned":[95],"model,":[98],"similarity":[100,161],"misclassified":[105],"samples":[106,156,168],"associated":[110,124],"with":[111,125],"predicted":[113],"labels":[114],"is":[115,157],"computed":[116],"analyze":[118],"whether":[119],"categories":[121],"wrongly":[123],"irrelevant":[126],"Finally,":[128],"issue":[132],"errors,":[136],"this":[137],"paper":[138],"proposes":[139],"an":[140],"incremental":[141],"learning":[142],"approach.":[143],"Two":[144],"prediction,":[149],"result":[152],"discrepant":[155,167],"determined":[158],"incorrect":[171],"corresponding":[175],"category":[176],"library.":[180],"experimental":[182],"results":[183],"indicate":[184],"that,":[185],"based":[186],"on":[187],"ImageNet-100":[189],"dataset,":[190],"our":[191],"improves":[193],"performance":[195],"1.":[197],"52%,":[198],"0.":[199,202],"54%":[200],"90%":[203],"GoogleNet,":[206],"Xception,":[207],"InceptionV3":[209],"models,":[210],"respectively.":[211]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}
