{"id":"https://openalex.org/W3004715525","doi":"https://doi.org/10.1109/smc42975.2020.9282812","title":"Towards Deep Machine Reasoning: a Prototype-based Deep Neural Network with Decision Tree Inference","display_name":"Towards Deep Machine Reasoning: a Prototype-based Deep Neural Network with Decision Tree Inference","publication_year":2020,"publication_date":"2020-10-11","ids":{"openalex":"https://openalex.org/W3004715525","doi":"https://doi.org/10.1109/smc42975.2020.9282812","mag":"3004715525"},"language":"en","primary_location":{"id":"doi:10.1109/smc42975.2020.9282812","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc42975.2020.9282812","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2002.03776","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039480864","display_name":"Plamen Angelov","orcid":"https://orcid.org/0000-0002-5770-934X"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Plamen Angelov","raw_affiliation_strings":["School of Computing and Communications, Lancaster University, Lancaster, UK","LANCASTER UNIVERSITY,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Communications, Lancaster University, Lancaster, UK","institution_ids":["https://openalex.org/I67415387"]},{"raw_affiliation_string":"LANCASTER UNIVERSITY,","institution_ids":["https://openalex.org/I67415387"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068590850","display_name":"Eduardo Soares","orcid":"https://orcid.org/0000-0002-2634-8270"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Eduardo Soares","raw_affiliation_strings":["School of Computing and Communications, Lancaster University, Lancaster, UK","LANCASTER UNIVERSITY,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Communications, Lancaster University, Lancaster, UK","institution_ids":["https://openalex.org/I67415387"]},{"raw_affiliation_string":"LANCASTER UNIVERSITY,","institution_ids":["https://openalex.org/I67415387"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0155747,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"9","issue":null,"first_page":"2092","last_page":"2099"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9995999932289124,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9995999932289124,"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":0.9993000030517578,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9957000017166138,"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/benchmark","display_name":"Benchmark (surveying)","score":0.7969211935997009},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7564343214035034},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7136551737785339},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.6677422523498535},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6459178924560547},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6419529914855957},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.523655116558075},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4856613874435425},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.4561220705509186},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44298216700553894},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4303112328052521},{"id":"https://openalex.org/keywords/decision-boundary","display_name":"Decision boundary","score":0.42774567008018494},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.17193534970283508},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07485923171043396}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7969211935997009},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7564343214035034},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7136551737785339},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6677422523498535},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6459178924560547},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6419529914855957},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.523655116558075},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4856613874435425},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.4561220705509186},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44298216700553894},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4303112328052521},{"id":"https://openalex.org/C42023084","wikidata":"https://www.wikidata.org/wiki/Q5249231","display_name":"Decision boundary","level":3,"score":0.42774567008018494},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.17193534970283508},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07485923171043396},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C155202549","wikidata":"https://www.wikidata.org/wiki/Q178803","display_name":"International trade","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":7,"locations":[{"id":"doi:10.1109/smc42975.2020.9282812","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc42975.2020.9282812","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2002.03776","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2002.03776","pdf_url":"https://arxiv.org/pdf/2002.03776","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:eprints.lancs.ac.uk:144752","is_oa":true,"landing_page_url":null,"pdf_url":"https://eprints.lancs.ac.uk/id/eprint/144752/1/2002.03776v1.pdf","source":{"id":"https://openalex.org/S4306401916","display_name":"Lancaster EPrints (Lancaster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67415387","host_organization_name":"Lancaster University","host_organization_lineage":["https://openalex.org/I67415387"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"NonPeerReviewed"},{"id":"mag:3004715525","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2002.03776.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:eprints.lancs.ac.uk:151143","is_oa":false,"landing_page_url":"https://eprints.lancs.ac.uk/id/eprint/151143/","pdf_url":null,"source":{"id":"https://openalex.org/S4306401916","display_name":"Lancaster EPrints (Lancaster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67415387","host_organization_name":"Lancaster University","host_organization_lineage":["https://openalex.org/I67415387"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Contribution in Book/Report/Proceedings"},{"id":"doi:10.48550/arxiv.2002.03776","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2002.03776","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"},{"id":"doi:10.13140/rg.2.2.20075.69926","is_oa":true,"landing_page_url":"https://doi.org/10.13140/rg.2.2.20075.69926","pdf_url":null,"source":null,"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":"pmh:oai:arXiv.org:2002.03776","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2002.03776","pdf_url":"https://arxiv.org/pdf/2002.03776","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.6100000143051147,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3004715525.pdf","grobid_xml":"https://content.openalex.org/works/W3004715525.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W648613739","https://openalex.org/W1530582330","https://openalex.org/W1554944419","https://openalex.org/W1576445103","https://openalex.org/W1596717185","https://openalex.org/W1686810756","https://openalex.org/W1849277567","https://openalex.org/W2017719567","https://openalex.org/W2035883480","https://openalex.org/W2097117768","https://openalex.org/W2099085757","https://openalex.org/W2106837051","https://openalex.org/W2108598243","https://openalex.org/W2109255472","https://openalex.org/W2140093718","https://openalex.org/W2158247472","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2557283755","https://openalex.org/W2574388714","https://openalex.org/W2896732351","https://openalex.org/W2929291387","https://openalex.org/W2964350391","https://openalex.org/W2977375814","https://openalex.org/W2989993634","https://openalex.org/W2993468200","https://openalex.org/W3007204104","https://openalex.org/W3021933602","https://openalex.org/W3033988828","https://openalex.org/W3041378136","https://openalex.org/W4250859275","https://openalex.org/W6631383626","https://openalex.org/W6634343353","https://openalex.org/W6637373629","https://openalex.org/W6639204139","https://openalex.org/W6676001293","https://openalex.org/W6684191040","https://openalex.org/W6768244331","https://openalex.org/W6773808382","https://openalex.org/W7015098694"],"related_works":["https://openalex.org/W3112857588","https://openalex.org/W2993468200","https://openalex.org/W3007204104","https://openalex.org/W2896579497","https://openalex.org/W3129917300","https://openalex.org/W2948638722","https://openalex.org/W3132602355","https://openalex.org/W2528523909","https://openalex.org/W2087189938","https://openalex.org/W2983991711","https://openalex.org/W2980748755","https://openalex.org/W3096450408","https://openalex.org/W3046330584","https://openalex.org/W2804148081","https://openalex.org/W2604476911","https://openalex.org/W2183647830","https://openalex.org/W2916773661","https://openalex.org/W3095787923","https://openalex.org/W2949541269","https://openalex.org/W823458229"],"abstract_inverted_index":{"In":[0,71,175],"this":[1],"paper":[2],"we":[3,127,148,177],"introduce":[4],"the":[5,31,36,75,88,103,110,116,120,130,133,141,158,167,216],"DMR":[6,52,195],"-":[7],"a":[8,20,56,64,99,125,179],"prototype-based":[9],"method":[10,40],"and":[11,26,107,203],"network":[12],"architecture":[13],"for":[14,157,184],"deep":[15],"learning":[16],"which":[17],"is":[18],"using":[19,98],"decision":[21,58],"tree":[22],"(DT)-":[23],"based":[24,59],"inference":[25],"synthetic":[27],"data":[28,114,210],"to":[29,77,101,206],"balance":[30],"classes.":[32],"It":[33,91],"builds":[34],"upon":[35],"recently":[37],"introduced":[38],"xDNN":[39],"addressing":[41],"more":[42],"complex":[43],"multi-class":[44,186],"problems,":[45],"specifically":[46,182],"when":[47],"classes":[48,62,81,111],"are":[49],"highly":[50],"imbalanced.":[51],"moves":[53],"away":[54],"from":[55,119,208],"direct":[57],"on":[60,140,172,188],"all":[61],"towards":[63],"layered":[65],"DT":[66,100],"of":[67,83,87,132,155,169],"pair-wise":[68],"class":[69,85,105],"comparisons.":[70],"addition,":[72],"it":[73],"forces":[74],"prototypes":[76,117,214],"be":[78],"balanced":[79],"between":[80],"regardless":[82],"possible":[84],"imbalances":[86],"training":[89,122],"data.":[90,123],"has":[92],"two":[93],"novel":[94],"mechanisms,":[95],"namely":[96],"i)":[97],"determine":[102],"winning":[104],"label,":[106],"ii)":[108],"balancing":[109],"by":[112,211],"synthesizing":[113],"around":[115],"determined":[118],"available":[121],"As":[124],"result,":[126],"improved":[128],"significantly":[129],"performance":[131],"resulting":[134],"fully":[135],"explainable":[136],"DNN":[137],"as":[138,163,165],"evidenced":[139],"well":[142,159,164,189],"know":[143],"benchmark":[144,192],"problem":[145],"Caltech-101.":[146],"Furthermore,":[147],"also":[149],"achieved":[150],"high":[151],"results":[152,168],"in":[153],"terms":[154],"accuracy":[156],"known":[160,190],"Caltech-256":[161],"dataset,":[162],"surpassed":[166],"other":[170],"approaches":[171],"Faces-1999":[173],"problem.":[174],"summary,":[176],"propose":[178],"new":[180,209,213],"approach":[181],"advantageous":[183],"imbalanced":[185],"problems":[187],"hard":[191],"datasets.":[193],"Moreover,":[194],"offers":[196],"full":[197,222],"explainability,":[198],"does":[199],"not":[200,220],"require":[201],"GPUs":[202],"can":[204],"continue":[205],"learn":[207],"adding":[212],"preserving":[215],"previous":[217],"ones":[218],"but":[219],"requiring":[221],"retraining.":[223]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
