{"id":"https://openalex.org/W4416251598","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227915","title":"On the choice of Vector Symbolic Architectures for Time Series Classification with HDC-MiniROCKET","display_name":"On the choice of Vector Symbolic Architectures for Time Series Classification with HDC-MiniROCKET","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251598","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227915"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227915","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227915","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 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/A5100695590","display_name":"M. Unger","orcid":"https://orcid.org/0000-0002-7651-0272"},"institutions":[{"id":"https://openalex.org/I2610724","display_name":"Chemnitz University of Technology","ror":"https://ror.org/00a208s56","country_code":"DE","type":"education","lineage":["https://openalex.org/I2610724"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Marcel Unger","raw_affiliation_strings":["Chemnitz University of Technology,Chemnitz,Germany"],"affiliations":[{"raw_affiliation_string":"Chemnitz University of Technology,Chemnitz,Germany","institution_ids":["https://openalex.org/I2610724"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053174463","display_name":"Kenny Schlegel","orcid":"https://orcid.org/0000-0001-8463-3902"},"institutions":[{"id":"https://openalex.org/I2610724","display_name":"Chemnitz University of Technology","ror":"https://ror.org/00a208s56","country_code":"DE","type":"education","lineage":["https://openalex.org/I2610724"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Kenny Schlegel","raw_affiliation_strings":["Chemnitz University of Technology,Chemnitz,Germany"],"affiliations":[{"raw_affiliation_string":"Chemnitz University of Technology,Chemnitz,Germany","institution_ids":["https://openalex.org/I2610724"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026465250","display_name":"Peter Protzel","orcid":"https://orcid.org/0000-0002-3870-7429"},"institutions":[{"id":"https://openalex.org/I2610724","display_name":"Chemnitz University of Technology","ror":"https://ror.org/00a208s56","country_code":"DE","type":"education","lineage":["https://openalex.org/I2610724"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Peter Protzel","raw_affiliation_strings":["Chemnitz University of Technology,Chemnitz,Germany"],"affiliations":[{"raw_affiliation_string":"Chemnitz University of Technology,Chemnitz,Germany","institution_ids":["https://openalex.org/I2610724"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5093054938","display_name":"Peer Neubert","orcid":null},"institutions":[{"id":"https://openalex.org/I16264642","display_name":"Koblenz University of Applied Sciences","ror":"https://ror.org/04j351e79","country_code":"DE","type":"education","lineage":["https://openalex.org/I16264642"]},{"id":"https://openalex.org/I4387153937","display_name":"Universit\u00e4t Koblenz","ror":"https://ror.org/0433e6t24","country_code":null,"type":"education","lineage":["https://openalex.org/I4387153937"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Peer Neubert","raw_affiliation_strings":["University of Koblenz,Koblenz,Germany"],"affiliations":[{"raw_affiliation_string":"University of Koblenz,Koblenz,Germany","institution_ids":["https://openalex.org/I16264642","https://openalex.org/I4387153937"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100695590"],"corresponding_institution_ids":["https://openalex.org/I2610724"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36539502,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9818999767303467,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9818999767303467,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.006300000008195639,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.003700000001117587,"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/benchmark","display_name":"Benchmark (surveying)","score":0.6990000009536743},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.6657000184059143},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5685999989509583},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4893999993801117},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.41359999775886536},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.3939000070095062}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6990000009536743},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6657000184059143},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5713000297546387},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5685999989509583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5059999823570251},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4893999993801117},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42649999260902405},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.41359999775886536},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3939000070095062},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33390000462532043},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3059999942779541},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.28380000591278076},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2628999948501587}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227915","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227915","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":34,"referenced_works":["https://openalex.org/W1650678991","https://openalex.org/W1839033316","https://openalex.org/W1971743472","https://openalex.org/W2070862086","https://openalex.org/W2117655830","https://openalex.org/W2117904045","https://openalex.org/W2157306293","https://openalex.org/W2157542201","https://openalex.org/W2167876429","https://openalex.org/W2188816045","https://openalex.org/W2555077524","https://openalex.org/W2796260258","https://openalex.org/W2963650979","https://openalex.org/W2974428947","https://openalex.org/W2982438846","https://openalex.org/W2988244882","https://openalex.org/W3001152281","https://openalex.org/W3173761280","https://openalex.org/W3175281730","https://openalex.org/W3175575864","https://openalex.org/W3202364339","https://openalex.org/W3211097789","https://openalex.org/W3214202924","https://openalex.org/W4205199576","https://openalex.org/W4225389525","https://openalex.org/W4234572080","https://openalex.org/W4293216350","https://openalex.org/W4294897967","https://openalex.org/W4312242794","https://openalex.org/W4376956372","https://openalex.org/W4391779661","https://openalex.org/W4392710444","https://openalex.org/W4394957446","https://openalex.org/W4402352525"],"related_works":[],"abstract_inverted_index":{"Hyperdimensional":[0],"Computing":[1],"(HDC)":[2],"has":[3],"shown":[4],"promise":[5],"in":[6,147],"time":[7,129,149],"series":[8,150],"classification":[9,51,117],"by":[10],"enhancing":[11],"MiniROCKET,":[12],"forming":[13],"HDC-MiniROCKET.":[14],"However,":[15],"the":[16,94,105,111],"impact":[17,49],"of":[18,69,93],"choosing":[19],"a":[20,27,65,84,91],"specific":[21],"HDC":[22],"implementation,":[23],"referred":[24],"to":[25,72,137,144],"as":[26],"Vector":[28],"Symbolic":[29],"Architecture":[30],"(VSA),":[31],"within":[32,45],"this":[33,81],"framework":[34],"is":[35],"so":[36],"far":[37],"unexplored.":[38],"This":[39],"paper":[40],"systematically":[41],"evaluates":[42],"different":[43],"VSAs":[44,63,124,146],"HDC-MiniROCKET,":[46],"analyzing":[47],"their":[48],"on":[50,83,104],"performance,":[52],"hyperparameter":[53,70],"sensitivity,":[54],"and":[55,87,116],"computational":[56,114],"efficiency.":[57],"Our":[58],"findings":[59],"reveal":[60],"that":[61,99,121],"certain":[62],"require":[64],"significantly":[66],"broader":[67],"range":[68],"values":[71],"achieve":[73,132],"optimal":[74],"accuracy":[75,136],"despite":[76],"similar":[77],"properties.":[78],"We":[79,119],"demonstrate":[80],"effect":[82],"synthetic":[85],"dataset":[86,106],"validate":[88],"it":[89],"across":[90],"subset":[92],"real-world":[95],"benchmark":[96],"UCR,":[97],"showing":[98],"VSA":[100],"performance":[101],"varies":[102],"depending":[103],"characteristics.":[107],"Additionally,":[108],"we":[109],"investigate":[110],"trade-off":[112],"between":[113],"complexity":[115],"accuracy.":[118],"find":[120],"computationally":[122],"efficient":[123],"not":[125],"only":[126],"reduce":[127],"processing":[128],"but":[130],"also":[131],"comparable":[133],"or":[134],"superior":[135],"more":[138],"complex":[139],"alternatives.":[140],"These":[141],"insights":[142],"help":[143],"select":[145],"HDC-based":[148],"classification.":[151]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}