{"id":"https://openalex.org/W3006469371","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206956","title":"Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neural Networks","display_name":"Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neural Networks","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3006469371","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206956","mag":"3006469371"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9206956","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206956","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/2002.04319","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030315975","display_name":"Gitesh Dawer","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153776","display_name":"Apple (United States)","ror":"https://ror.org/059hsda18","country_code":"US","type":"company","lineage":["https://openalex.org/I4210153776"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gitesh Dawer","raw_affiliation_strings":["CoreML Group, Apple Inc, Cupertino, California, USA","Apple Inc,CoreML Group,Cupertino,California,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CoreML Group, Apple Inc, Cupertino, California, USA","institution_ids":["https://openalex.org/I4210153776"]},{"raw_affiliation_string":"Apple Inc,CoreML Group,Cupertino,California,USA","institution_ids":["https://openalex.org/I4210153776"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005966448","display_name":"Yangzi Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yangzi Guo","raw_affiliation_strings":["Department of Mathematics, Florida State University, Tallahassee, Florida, USA","FLORIDA STATE UNIV"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Florida State University, Tallahassee, Florida, USA","institution_ids":["https://openalex.org/I103163165"]},{"raw_affiliation_string":"FLORIDA STATE UNIV","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101419627","display_name":"Sida Liu","orcid":"https://orcid.org/0000-0003-3716-8360"},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sida Liu","raw_affiliation_strings":["Department of Statistics, Florida State University, Tallahassee, Florida, USA","FLORIDA STATE UNIV"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Florida State University, Tallahassee, Florida, USA","institution_ids":["https://openalex.org/I103163165"]},{"raw_affiliation_string":"FLORIDA STATE UNIV","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052175239","display_name":"Adrian Barbu","orcid":"https://orcid.org/0000-0002-9548-7872"},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Adrian Barbu","raw_affiliation_strings":["Department of Statistics, Florida State University, Tallahassee, Florida, USA","FLORIDA STATE UNIV"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Florida State University, Tallahassee, Florida, USA","institution_ids":["https://openalex.org/I103163165"]},{"raw_affiliation_string":"FLORIDA STATE UNIV","institution_ids":[]}]}],"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":0.01741927,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9983000159263611,"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/T10320","display_name":"Neural Networks and Applications","score":0.9983000159263611,"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":0.9983000159263611,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/overfitting","display_name":"Overfitting","score":0.8409178853034973},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7566579580307007},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6821444034576416},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6789852380752563},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.6348990797996521},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.5925496220588684},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5804964900016785},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5718175172805786},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.5469236373901367},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4329758286476135},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.42799991369247437},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09855484962463379}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8409178853034973},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7566579580307007},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6821444034576416},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6789852380752563},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.6348990797996521},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.5925496220588684},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5804964900016785},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5718175172805786},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.5469236373901367},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4329758286476135},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.42799991369247437},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09855484962463379},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9206956","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206956","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2002.04319","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2002.04319","pdf_url":"https://arxiv.org/pdf/2002.04319","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":"doi:10.48550/arxiv.2002.04319","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2002.04319","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":"Preprint"},{"id":"mag:3006469371","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2002.04319","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2002.04319","pdf_url":"https://arxiv.org/pdf/2002.04319","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":0.7699999809265137,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3006469371.pdf","grobid_xml":"https://content.openalex.org/works/W3006469371.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W308556676","https://openalex.org/W1504694836","https://openalex.org/W1528227026","https://openalex.org/W1572401739","https://openalex.org/W1678356000","https://openalex.org/W1857789879","https://openalex.org/W1988115241","https://openalex.org/W2048231652","https://openalex.org/W2083798294","https://openalex.org/W2112865076","https://openalex.org/W2125055259","https://openalex.org/W2152799677","https://openalex.org/W2182722412","https://openalex.org/W2220384803","https://openalex.org/W2290283816","https://openalex.org/W2294581849","https://openalex.org/W2399500811","https://openalex.org/W2517630287","https://openalex.org/W2524838801","https://openalex.org/W2593649365","https://openalex.org/W2738004995","https://openalex.org/W2739501612","https://openalex.org/W2911964244","https://openalex.org/W2963152037","https://openalex.org/W2963292722","https://openalex.org/W2964121744","https://openalex.org/W3120740533","https://openalex.org/W4252684946","https://openalex.org/W6631190155","https://openalex.org/W6682557032","https://openalex.org/W6696356757","https://openalex.org/W6712836611","https://openalex.org/W6726395616","https://openalex.org/W6733975163","https://openalex.org/W6741886175"],"related_works":["https://openalex.org/W3089629888","https://openalex.org/W2797354382","https://openalex.org/W3149933339","https://openalex.org/W1956952929","https://openalex.org/W3197392674","https://openalex.org/W3208380497","https://openalex.org/W2257987991","https://openalex.org/W2992348704","https://openalex.org/W3036194310","https://openalex.org/W1599216479","https://openalex.org/W1485807555","https://openalex.org/W2206135308","https://openalex.org/W209072243","https://openalex.org/W3090928696","https://openalex.org/W131552660","https://openalex.org/W2560512695","https://openalex.org/W2149256131","https://openalex.org/W2610903631","https://openalex.org/W2493224314","https://openalex.org/W1974004482"],"abstract_inverted_index":{"Artificial":[0],"Neural":[1],"Networks":[2],"form":[3],"the":[4,90,100,116,126],"basis":[5],"of":[6,19,94,109,115,128],"very":[7],"powerful":[8],"learning":[9,108],"methods.":[10],"It":[11],"has":[12],"been":[13],"observed":[14],"that":[15],"a":[16,41,78,85],"naive":[17],"application":[18],"fully":[20,95,133],"connected":[21,96,134],"neural":[22,86,97,135],"networks":[23,136],"to":[24,32,37,45,68,80,113],"data":[25],"with":[26],"many":[27],"irrelevant":[28],"variables":[29],"often":[30],"leads":[31],"overfitting.":[33],"In":[34,61],"an":[35,130],"attempt":[36],"circumvent":[38],"this":[39,62],"issue,":[40],"prior":[42],"knowledge":[43],"pertaining":[44],"what":[46],"features":[47,72],"are":[48],"relevant":[49,71],"and":[50,73,76,122,137],"their":[51,74],"possible":[52],"feature":[53,104],"interactions":[54,75],"can":[55],"be":[56],"encoded":[57],"into":[58,84],"these":[59],"networks.":[60,98],"work,":[63],"we":[64],"use":[65],"decision":[66],"trees":[67],"capture":[69],"such":[70,129],"define":[77],"mapping":[79],"encode":[81],"extracted":[82],"relationships":[83],"network.":[87],"This":[88],"addresses":[89],"initialization":[91],"related":[92],"concerns":[93],"At":[99],"same":[101],"time":[102],"through":[103],"selection":[105],"it":[106],"enables":[107],"compact":[110],"representations":[111],"compared":[112],"state":[114],"art":[117],"tree-based":[118,138],"approaches.":[119,139],"Empirical":[120],"evaluations":[121],"simulation":[123],"studies":[124],"show":[125],"superiority":[127],"approach":[131],"over":[132]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2022-07-26T00:00:00"}
