{"id":"https://openalex.org/W2998685550","doi":"https://doi.org/10.1109/icbk.2019.00041","title":"A Fast Relative Density Method Based on Space Partitioning","display_name":"A Fast Relative Density Method Based on Space Partitioning","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W2998685550","doi":"https://doi.org/10.1109/icbk.2019.00041","mag":"2998685550"},"language":"en","primary_location":{"id":"doi:10.1109/icbk.2019.00041","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icbk.2019.00041","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Knowledge (ICBK)","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/A5011891097","display_name":"Binggui Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Binggui Wang","raw_affiliation_strings":["Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060987351","display_name":"Shuyin Xia","orcid":"https://orcid.org/0000-0001-5993-9563"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuyin Xia","raw_affiliation_strings":["Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003746498","display_name":"Hong Yu","orcid":"https://orcid.org/0000-0003-0667-8413"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Yu","raw_affiliation_strings":["Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031220156","display_name":"Guoyin Wang","orcid":"https://orcid.org/0000-0002-8521-5232"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoyin Wang","raw_affiliation_strings":["Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5011891097"],"corresponding_institution_ids":["https://openalex.org/I10535382"],"apc_list":null,"apc_paid":null,"fwci":0.14,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.59579985,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"28","issue":null,"first_page":"250","last_page":"256"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9998999834060669,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9998999834060669,"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/T10057","display_name":"Face and Expression Recognition","score":0.9911999702453613,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9858999848365784,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.8291429281234741},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.7956275939941406},{"id":"https://openalex.org/keywords/relative-density","display_name":"Relative density","score":0.7226434350013733},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6497496366500854},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6344990730285645},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.5506439208984375},{"id":"https://openalex.org/keywords/efficiency","display_name":"Efficiency","score":0.5488149523735046},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49090105295181274},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4613056778907776},{"id":"https://openalex.org/keywords/approximation-error","display_name":"Approximation error","score":0.4309212863445282},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.42184486985206604},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35742777585983276},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3073817789554596},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22416770458221436},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.12159150838851929},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.09220147132873535}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8291429281234741},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.7956275939941406},{"id":"https://openalex.org/C82455628","wikidata":"https://www.wikidata.org/wiki/Q11027905","display_name":"Relative density","level":3,"score":0.7226434350013733},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6497496366500854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6344990730285645},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.5506439208984375},{"id":"https://openalex.org/C17648541","wikidata":"https://www.wikidata.org/wiki/Q2265984","display_name":"Efficiency","level":3,"score":0.5488149523735046},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49090105295181274},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4613056778907776},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.4309212863445282},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.42184486985206604},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35742777585983276},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3073817789554596},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22416770458221436},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.12159150838851929},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.09220147132873535},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C2777581544","wikidata":"https://www.wikidata.org/wiki/Q844613","display_name":"Sintering","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icbk.2019.00041","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icbk.2019.00041","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Knowledge (ICBK)","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":30,"referenced_works":["https://openalex.org/W72116547","https://openalex.org/W1514928307","https://openalex.org/W1666146316","https://openalex.org/W1667009198","https://openalex.org/W1898031563","https://openalex.org/W1964047516","https://openalex.org/W1973948212","https://openalex.org/W1975128126","https://openalex.org/W1977556410","https://openalex.org/W1992974059","https://openalex.org/W2008056655","https://openalex.org/W2008183828","https://openalex.org/W2034841618","https://openalex.org/W2107483511","https://openalex.org/W2124776405","https://openalex.org/W2157550316","https://openalex.org/W2167460663","https://openalex.org/W2170645625","https://openalex.org/W2277132981","https://openalex.org/W2284177844","https://openalex.org/W2295598076","https://openalex.org/W2418905409","https://openalex.org/W2498119267","https://openalex.org/W2589453516","https://openalex.org/W2758828962","https://openalex.org/W2790117078","https://openalex.org/W2804975550","https://openalex.org/W2913728419","https://openalex.org/W2964292098","https://openalex.org/W3102476541"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W4378510483","https://openalex.org/W4384382773"],"abstract_inverted_index":{"Label":[0],"noise":[1,27,83],"play":[2],"an":[3,60],"important":[4],"role":[5],"in":[6,25,99],"classification.":[7],"It":[8],"can":[9],"cause":[10],"overfitting":[11],"of":[12],"learning":[13],"methods":[14,130],"and":[15,131],"deteriorate":[16],"their":[17],"generalizability.":[18],"The":[19,75,112],"relative":[20,41,62,67,102,138],"density":[21,42,63,68,103,139],"method":[22,43,69,77,110,123],"is":[23,52,97,105],"effective":[24],"label":[26,82],"detection,":[28],"but":[29,48,85],"it":[30],"has":[31,124],"high":[32],"time":[33,46],"complexity.":[34],"On":[35],"the":[36,39,45,49,66,81,93,100,108,116,121,128,136],"other":[37],"hand,":[38],"multi-granularity":[40,137],"reduces":[44],"cost,":[47],"classification":[50,90,133],"accuracy":[51,134],"also":[53,86],"reduced.":[54],"In":[55],"this":[56],"paper,":[57],"we":[58],"propose":[59],"improved":[61],"method,":[64],"named":[65],"based":[70],"on":[71,115],"space":[72],"partitioning":[73],"(SPRD).":[74],"proposed":[76,109,122],"not":[78],"only":[79],"accelerates":[80],"detection":[84],"maintains":[87],"a":[88],"good":[89],"performance.":[91],"Also,":[92],"parameter":[94],"k,":[95],"which":[96],"used":[98],"conventional":[101,129],"methods,":[104],"removed,":[106],"making":[107],"adaptive.":[111],"experiment":[113],"results":[114],"UCI":[117],"datasets":[118],"demonstrate":[119],"that":[120],"higher":[125],"efficiency":[126],"than":[127,135],"better":[132],"method.":[140]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
