{"id":"https://openalex.org/W3199535709","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533833","title":"A Study of Local Optima for Learning Feature Interactions using Neural Networks","display_name":"A Study of Local Optima for Learning Feature Interactions using Neural Networks","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3199535709","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533833","mag":"3199535709"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9533833","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533833","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 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/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":true,"raw_author_name":"Yangzi Guo","raw_affiliation_strings":["Florida State University, Tallahassee, Florida, USA"],"affiliations":[{"raw_affiliation_string":"Florida State University, Tallahassee, Florida, USA","institution_ids":["https://openalex.org/I103163165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101780958","display_name":"Ying Wu","orcid":"https://orcid.org/0009-0001-6768-5118"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ying Nian Wu","raw_affiliation_strings":["University of California, Los Angeles Los Angeles, California, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Los Angeles Los Angeles, California, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"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":["Florida State University, Tallahassee, Florida, USA"],"affiliations":[{"raw_affiliation_string":"Florida State University, Tallahassee, Florida, USA","institution_ids":["https://openalex.org/I103163165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5005966448"],"corresponding_institution_ids":["https://openalex.org/I103163165"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12865459,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"15","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":0.9995999932289124,"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/T10320","display_name":"Neural Networks and Applications","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/T10036","display_name":"Advanced Neural Network Applications","score":0.9958000183105469,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","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/maxima-and-minima","display_name":"Maxima and minima","score":0.8741047382354736},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6513245701789856},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6064502000808716},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6014344692230225},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.5947919487953186},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5649600625038147},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.563712477684021},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.5141909122467041},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5137201547622681},{"id":"https://openalex.org/keywords/local-optimum","display_name":"Local optimum","score":0.46481260657310486},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45211634039878845},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.44858407974243164},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40095940232276917},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25253236293792725}],"concepts":[{"id":"https://openalex.org/C186633575","wikidata":"https://www.wikidata.org/wiki/Q845060","display_name":"Maxima and minima","level":2,"score":0.8741047382354736},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6513245701789856},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6064502000808716},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6014344692230225},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.5947919487953186},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5649600625038147},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.563712477684021},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.5141909122467041},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5137201547622681},{"id":"https://openalex.org/C141934464","wikidata":"https://www.wikidata.org/wiki/Q3305386","display_name":"Local optimum","level":2,"score":0.46481260657310486},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45211634039878845},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.44858407974243164},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40095940232276917},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25253236293792725},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9533833","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533833","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8899999856948853}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2095705004","https://openalex.org/W2255548690","https://openalex.org/W2399994860","https://openalex.org/W2788838181","https://openalex.org/W2793333878","https://openalex.org/W2805003733","https://openalex.org/W2949117887","https://openalex.org/W2951569836","https://openalex.org/W2963384892","https://openalex.org/W2963674932","https://openalex.org/W2963813662","https://openalex.org/W2964121744","https://openalex.org/W2964299589","https://openalex.org/W3120740533","https://openalex.org/W6674330103","https://openalex.org/W6748173244","https://openalex.org/W6751979845"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W2888392564","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W4390569940","https://openalex.org/W4361193272","https://openalex.org/W2963326959","https://openalex.org/W4388685194","https://openalex.org/W4312407344","https://openalex.org/W2894289927"],"abstract_inverted_index":{"In":[0,39,70],"many":[1,136],"fields":[2],"such":[3],"as":[4],"bioinformatics,":[5],"high":[6],"energy":[7],"physics,":[8],"power":[9],"distribution,":[10],"etc.,":[11],"it":[12],"is":[13,32,84,96],"desirable":[14],"to":[15,35,65,98,179],"learn":[16],"non-linear":[17,52],"models":[18],"where":[19,81],"a":[20,67,82,87,166,197,211],"small":[21,89,212],"number":[22,142,150,213],"of":[23,62,91,108,143,151,177,196,214],"variables":[24],"are":[25,187],"selected":[26],"and":[27,113,140,160,169,219],"the":[28,37,78,118,128,141,149,157,175,194],"interaction":[29],"between":[30],"them":[31],"explicitly":[33],"modeled":[34],"predict":[36],"response.":[38],"principle,":[40],"neural":[41],"networks":[42,209],"(NNs)":[43],"could":[44],"accomplish":[45],"this":[46,71],"task":[47],"since":[48],"they":[49],"can":[50,202],"model":[51],"feature":[53,75,111,114,162,170],"interactions":[54,115],"very":[55],"well.":[56],"However,":[57],"NNs":[58,178],"require":[59],"large":[60],"amounts":[61],"training":[63,92],"data":[64,109,120,134],"have":[66],"good":[68,217],"generalization.":[69],"paper":[72],"we":[73,116,164,191],"study":[74,117],"selection":[76,95,112,163,171],"in":[77],"data-starved":[79],"regime":[80],"NN":[83,198],"trained":[85],"on":[86,132,199,210],"relatively":[88],"amount":[90],"data.":[93],"Feature":[94],"known":[97],"improve":[99],"generalization":[100],"for":[101,161],"linear":[102],"models.":[103],"As":[104],"an":[105],"extreme":[106],"case":[107],"with":[110,121,148,156,216],"XOR-like":[119,133],"irrelevant":[122,152,188],"variables.":[123,153,189],"We":[124],"experimentally":[125],"observed":[126],"that":[127,173,193],"cross-entropy":[129],"loss":[130],"function":[131],"has":[135],"non-equivalent":[137],"local":[138,144,158,182],"minima,":[139],"minima":[145,159,183],"grows":[146],"exponentially":[147],"To":[154],"deal":[155],"propose":[165],"node":[167],"pruning":[168],"algorithm":[172],"improves":[174],"capability":[176],"find":[180],"better":[181],"even":[184],"when":[185],"there":[186],"Finally,":[190],"show":[192],"performance":[195],"real":[200],"datasets":[201],"be":[203],"improved":[204],"using":[205],"pruning,":[206],"obtaining":[207],"compact":[208],"features,":[215],"prediction":[218],"interpretability.":[220]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}