{"id":"https://openalex.org/W7125972623","doi":"https://doi.org/10.1109/smc58881.2025.11343492","title":"Enhancing the Dendritic Cell Algorithm through Automated Feature Reduction Techniques for Improved Anomaly Detection","display_name":"Enhancing the Dendritic Cell Algorithm through Automated Feature Reduction Techniques for Improved Anomaly Detection","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W7125972623","doi":"https://doi.org/10.1109/smc58881.2025.11343492"},"language":null,"primary_location":{"id":"doi:10.1109/smc58881.2025.11343492","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343492","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5124133162","display_name":"Vitor Pereira","orcid":null},"institutions":[{"id":"https://openalex.org/I182534213","display_name":"Universidade do Porto","ror":"https://ror.org/043pwc612","country_code":"PT","type":"education","lineage":["https://openalex.org/I182534213"]}],"countries":["PT"],"is_corresponding":true,"raw_author_name":"Vitor Pereira","raw_affiliation_strings":["Universidade do Porto,Faculdade de Engenharia,Department of Informatics Engineering,Porto,Portugal,4200-465"],"affiliations":[{"raw_affiliation_string":"Universidade do Porto,Faculdade de Engenharia,Department of Informatics Engineering,Porto,Portugal,4200-465","institution_ids":["https://openalex.org/I182534213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038662879","display_name":"Rui Pinto","orcid":"https://orcid.org/0000-0002-0345-1208"},"institutions":[{"id":"https://openalex.org/I182534213","display_name":"Universidade do Porto","ror":"https://ror.org/043pwc612","country_code":"PT","type":"education","lineage":["https://openalex.org/I182534213"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Rui Pinto","raw_affiliation_strings":["Universidade do Porto,SYSTEC, ARISE, Faculdade de Engenharia,Porto,Portugal,4200-465"],"affiliations":[{"raw_affiliation_string":"Universidade do Porto,SYSTEC, ARISE, Faculdade de Engenharia,Porto,Portugal,4200-465","institution_ids":["https://openalex.org/I182534213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008404992","display_name":"Gil Gon\u00e7alves","orcid":"https://orcid.org/0000-0001-7757-7308"},"institutions":[{"id":"https://openalex.org/I182534213","display_name":"Universidade do Porto","ror":"https://ror.org/043pwc612","country_code":"PT","type":"education","lineage":["https://openalex.org/I182534213"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Gil Gon\u00e7alves","raw_affiliation_strings":["Universidade do Porto,SYSTEC, ARISE, Faculdade de Engenharia,Porto,Portugal,4200-465"],"affiliations":[{"raw_affiliation_string":"Universidade do Porto,SYSTEC, ARISE, Faculdade de Engenharia,Porto,Portugal,4200-465","institution_ids":["https://openalex.org/I182534213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5124133162"],"corresponding_institution_ids":["https://openalex.org/I182534213"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.61848239,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3685","last_page":"3690"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12391","display_name":"Artificial Immune Systems Applications","score":0.9114000201225281,"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"}},"topics":[{"id":"https://openalex.org/T12391","display_name":"Artificial Immune Systems Applications","score":0.9114000201225281,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.02810000069439411,"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/T12576","display_name":"vaccines and immunoinformatics approaches","score":0.0215000007301569,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7317000031471252},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6455000042915344},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6409000158309937},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.5436000227928162},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5260999798774719},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5203999876976013},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5065000057220459},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4413999915122986},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.4244000017642975}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7317000031471252},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6852999925613403},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6455000042915344},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6409000158309937},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6227999925613403},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.5436000227928162},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5260999798774719},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5203999876976013},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5065000057220459},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4413999915122986},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.4244000017642975},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4185999929904938},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3788999915122986},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.36570000648498535},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.3521000146865845},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.34779998660087585},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.32510000467300415},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3244999945163727},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.3158999979496002},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C93768804","wikidata":"https://www.wikidata.org/wiki/Q2518735","display_name":"Artificial immune system","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2915000021457672},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.2897999882698059},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.265500009059906},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2624000012874603},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.2547000050544739},{"id":"https://openalex.org/C90119067","wikidata":"https://www.wikidata.org/wiki/Q43260","display_name":"Polynomial","level":2,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc58881.2025.11343492","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343492","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals","score":0.43800774216651917}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W168647982","https://openalex.org/W1703006379","https://openalex.org/W1767780060","https://openalex.org/W1958350149","https://openalex.org/W1992961908","https://openalex.org/W2084006363","https://openalex.org/W2118599924","https://openalex.org/W2128773324","https://openalex.org/W2139772053","https://openalex.org/W2145952599","https://openalex.org/W2151317568","https://openalex.org/W2280057945","https://openalex.org/W2335649250","https://openalex.org/W2752320209","https://openalex.org/W2789828921","https://openalex.org/W2950710988","https://openalex.org/W2964300371","https://openalex.org/W3029399597","https://openalex.org/W3092503698","https://openalex.org/W3122880803","https://openalex.org/W3185609876","https://openalex.org/W4225253195"],"related_works":[],"abstract_inverted_index":{"The":[0,68],"Dendritic":[1],"Cell":[2],"Algorithm":[3],"(DCA),":[4],"inspired":[5],"by":[6],"the":[7,38,88,126,139],"Human":[8],"Immune":[9,15],"System,":[10],"is":[11],"a":[12,62,84],"promising":[13],"Artificial":[14],"System":[16],"(AIS)":[17],"for":[18],"anomaly":[19],"detection.":[20,122],"However,":[21],"its":[22],"pre-processing":[23],"phase":[24],"traditionally":[25],"depends":[26,136],"on":[27,72,138],"expert":[28],"manual":[29],"intervention,":[30],"limiting":[31],"scalability":[32],"and":[33,61,78,95,120,143],"objectivity.":[34],"This":[35],"study":[36],"investigates":[37],"impact":[39],"of":[40,128],"integrating":[41],"automated":[42,129],"feature":[43,140],"reduction":[44,141],"techniques":[45],"to":[46],"streamline":[47],"this":[48],"phase.":[49],"We":[50],"propose":[51],"three":[52],"approaches:":[53],"Kernel":[54],"Principal":[55],"Component":[56],"Analysis":[57],"(KPCA),":[58],"Autoencoders":[59],"(AE),":[60],"hybrid":[63],"Autoencoder":[64],"with":[65,83,107,111],"KPCA":[66],"(AEkPCA).":[67],"models":[69],"were":[70],"tested":[71],"seven":[73],"datasets":[74],"from":[75],"cybersecurity,":[76],"biology,":[77],"finance":[79],"domains.":[80],"KPCA,":[81],"particularly":[82],"Gaussian":[85],"kernel,":[86],"delivered":[87],"most":[89],"consistent":[90],"results,":[91],"achieving":[92],"high":[93],"accuracy":[94],"strong":[96],"MCAV":[97],"separation.":[98],"AEkPCA":[99],"showed":[100],"competitive":[101],"performance":[102,135],"in":[103,115,131],"complex":[104],"datasets,":[105],"especially":[106],"polynomial":[108],"kernels,":[109],"though":[110],"greater":[112],"variability.":[113],"AE":[114],"isolation":[116],"exhibited":[117],"unstable":[118],"behavior":[119],"inconsistent":[121],"These":[123],"results":[124],"support":[125],"viability":[127],"preprocessing":[130],"DCA,":[132],"highlighting":[133],"that":[134],"heavily":[137],"method":[142],"kernel":[144],"combination.":[145]},"counts_by_year":[],"updated_date":"2026-01-29T23:17:01.242718","created_date":"2026-01-29T00:00:00"}
