{"id":"https://openalex.org/W3083377258","doi":"https://doi.org/10.1109/cec48606.2020.9185626","title":"Integrated Learning Method for Anomaly Detection Combining KLSH and Isolation Principles","display_name":"Integrated Learning Method for Anomaly Detection Combining KLSH and Isolation Principles","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3083377258","doi":"https://doi.org/10.1109/cec48606.2020.9185626","mag":"3083377258"},"language":"en","primary_location":{"id":"doi:10.1109/cec48606.2020.9185626","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec48606.2020.9185626","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Congress on Evolutionary Computation (CEC)","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/A5061292482","display_name":"Hongchun Qu","orcid":"https://orcid.org/0000-0001-7623-2383"},"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":"Hongchun Qu","raw_affiliation_strings":["College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061795687","display_name":"Zonglan Li","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":false,"raw_author_name":"Zonglan Li","raw_affiliation_strings":["College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006021558","display_name":"Jingjing Wu","orcid":"https://orcid.org/0000-0003-1708-8996"},"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":"Jingjing Wu","raw_affiliation_strings":["College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5061292482"],"corresponding_institution_ids":["https://openalex.org/I10535382"],"apc_list":null,"apc_paid":null,"fwci":0.2651,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.63230841,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"46 20","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12391","display_name":"Artificial Immune Systems Applications","score":0.9871000051498413,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/anomaly-detection","display_name":"Anomaly detection","score":0.8038493394851685},{"id":"https://openalex.org/keywords/locality-sensitive-hashing","display_name":"Locality-sensitive hashing","score":0.6093069314956665},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5991230607032776},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5594447255134583},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5527297258377075},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5414599776268005},{"id":"https://openalex.org/keywords/gaussian-function","display_name":"Gaussian function","score":0.4546703100204468},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4447934329509735},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.41320890188217163},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.3738095462322235},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.370053768157959},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3418287932872772},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3358956575393677},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24929675459861755},{"id":"https://openalex.org/keywords/hash-table","display_name":"Hash table","score":0.07688197493553162}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8038493394851685},{"id":"https://openalex.org/C74270461","wikidata":"https://www.wikidata.org/wiki/Q1625299","display_name":"Locality-sensitive hashing","level":4,"score":0.6093069314956665},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5991230607032776},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5594447255134583},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5527297258377075},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5414599776268005},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.4546703100204468},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4447934329509735},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.41320890188217163},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.3738095462322235},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.370053768157959},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3418287932872772},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3358956575393677},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24929675459861755},{"id":"https://openalex.org/C67388219","wikidata":"https://www.wikidata.org/wiki/Q207440","display_name":"Hash table","level":3,"score":0.07688197493553162},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cec48606.2020.9185626","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec48606.2020.9185626","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Congress on Evolutionary Computation (CEC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6399999856948853,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1598260899","https://openalex.org/W1740377143","https://openalex.org/W1870428314","https://openalex.org/W1970978220","https://openalex.org/W1974561058","https://openalex.org/W1986332411","https://openalex.org/W1990537115","https://openalex.org/W1995443851","https://openalex.org/W2062417985","https://openalex.org/W2108995755","https://openalex.org/W2122646361","https://openalex.org/W2129281431","https://openalex.org/W2144182447","https://openalex.org/W2146384339","https://openalex.org/W2147717514","https://openalex.org/W2160200253","https://openalex.org/W2162006472","https://openalex.org/W2164421921","https://openalex.org/W2296719434","https://openalex.org/W2536704314","https://openalex.org/W2613577378","https://openalex.org/W2616332741","https://openalex.org/W2773657144","https://openalex.org/W2783205364","https://openalex.org/W2786535488","https://openalex.org/W2808201204","https://openalex.org/W2884526582","https://openalex.org/W2912097776","https://openalex.org/W2963887617","https://openalex.org/W2972614519","https://openalex.org/W2988244882","https://openalex.org/W6635723885","https://openalex.org/W6639283537","https://openalex.org/W6642796196","https://openalex.org/W6666507233","https://openalex.org/W6738056044","https://openalex.org/W6758523849"],"related_works":["https://openalex.org/W3020818756","https://openalex.org/W2144265691","https://openalex.org/W2952368993","https://openalex.org/W2033383639","https://openalex.org/W3108918257","https://openalex.org/W3016124764","https://openalex.org/W2010970209","https://openalex.org/W4225523385","https://openalex.org/W1992155600","https://openalex.org/W4281735597"],"abstract_inverted_index":{"Aiming":[0],"at":[1],"the":[2,5,47,54,57,63,87,93,99,116,127,137,141,159],"problem":[3,94],"that":[4,28,154],"Isolated":[6],"Forest":[7],"(iForest)":[8],"has":[9,156],"low":[10],"local":[11,69,130],"anomaly":[12,25,84,131,164],"detection":[13,26,85,165],"accuracy":[14,128,160],"in":[15,166],"highdimensional":[16,167],"and":[17,39,68,103,140,161,168],"massive":[18,169],"data":[19,55,60,89,149,170],"sets,":[20],"this":[21,44,107],"paper":[22,108],"proposes":[23,109],"an":[24],"method":[27,45],"combines":[29],"locality-sensitive":[30],"hashing":[31],"algorithm":[32,139],"based":[33,144],"on":[34,77,86,145,147],"Gaussian":[35],"Kernel":[36],"Function":[37],"(KLSH)":[38],"means-optimized":[40],"iForest":[41,79,119],"algorithm.":[42],"In":[43],"(KLSH+iForest),":[46],"kernel":[48],"function":[49],"is":[50,80],"used":[51],"to":[52,62,82,97,120],"map":[53],"from":[56],"linearly":[58,64],"indivisible":[59],"space":[61],"separable":[65],"feature":[66],"space,":[67],"anomalies":[70],"are":[71],"converted":[72],"into":[73],"global":[74,122],"anomalies.":[75],"Based":[76],"above,":[78],"constructed":[81],"perform":[83],"Kernelized":[88],"sets.":[90,150,171],"To":[91],"solve":[92],"of":[95,118,129,163],"how":[96],"select":[98],"optimal":[100],"segmentation":[101],"attributes":[102],"values":[104],"for":[105],"iForest,":[106],"a":[110],"mean":[111],"optimization":[112],"strategy.":[113],"While":[114],"maintaining":[115],"ability":[117],"detect":[121],"anomalies,":[123],"KLSH+iForest":[124,135,155],"also":[125],"improves":[126],"detection.":[132],"We":[133],"compare":[134],"with":[136],"LOF":[138],"improved":[142,158],"algorithms":[143],"LSH":[146],"public":[148],"Experimental":[151],"results":[152],"show":[153],"significantly":[157],"efficiency":[162]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
