{"id":"https://openalex.org/W4407574880","doi":"https://doi.org/10.1007/s10618-024-01085-0","title":"Proximity forest 2.0: a new effective and scalable similarity-based classifier for time series","display_name":"Proximity forest 2.0: a new effective and scalable similarity-based classifier for time series","publication_year":2025,"publication_date":"2025-02-14","ids":{"openalex":"https://openalex.org/W4407574880","doi":"https://doi.org/10.1007/s10618-024-01085-0"},"language":"en","primary_location":{"id":"doi:10.1007/s10618-024-01085-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-024-01085-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-024-01085-0.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10618-024-01085-0.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088038766","display_name":"Chang Wei Tan","orcid":"https://orcid.org/0000-0001-8377-3241"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Chang Wei Tan","raw_affiliation_strings":["Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton Campus, Woodside Building, 20 Exhibition Walk, Melbourne, VIC, 3800, Australia"],"raw_orcid":"https://orcid.org/0000-0001-8377-3241","affiliations":[{"raw_affiliation_string":"Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton Campus, Woodside Building, 20 Exhibition Walk, Melbourne, VIC, 3800, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024628790","display_name":"Matthieu Herrmann","orcid":"https://orcid.org/0000-0002-0074-470X"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Matthieu Herrmann","raw_affiliation_strings":["Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton Campus, Woodside Building, 20 Exhibition Walk, Melbourne, VIC, 3800, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton Campus, Woodside Building, 20 Exhibition Walk, Melbourne, VIC, 3800, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019440770","display_name":"Mahsa Salehi","orcid":"https://orcid.org/0000-0002-2991-1612"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Mahsa Salehi","raw_affiliation_strings":["Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton Campus, Woodside Building, 20 Exhibition Walk, Melbourne, VIC, 3800, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton Campus, Woodside Building, 20 Exhibition Walk, Melbourne, VIC, 3800, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058054791","display_name":"Geoffrey I. Webb","orcid":"https://orcid.org/0000-0001-9963-5169"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Geoffrey I. Webb","raw_affiliation_strings":["Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton Campus, Woodside Building, 20 Exhibition Walk, Melbourne, VIC, 3800, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton Campus, Woodside Building, 20 Exhibition Walk, Melbourne, VIC, 3800, Australia","institution_ids":["https://openalex.org/I56590836"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5088038766"],"corresponding_institution_ids":["https://openalex.org/I56590836"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":5.2498,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.95165446,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"39","issue":"2","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11309","display_name":"Music and Audio Processing","score":0.9575999975204468,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9535999894142151,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6572572588920593},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6270446181297302},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5776503086090088},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5410846471786499},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47474342584609985},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47394636273384094},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4360710680484772},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4206535220146179},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.393582820892334},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38230788707733154},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.12641364336013794},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.05164378881454468}],"concepts":[{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6572572588920593},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6270446181297302},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5776503086090088},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5410846471786499},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47474342584609985},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47394636273384094},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4360710680484772},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4206535220146179},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.393582820892334},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38230788707733154},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.12641364336013794},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.05164378881454468},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s10618-024-01085-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-024-01085-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-024-01085-0.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},{"id":"pmh:oai:monash.edu:publications/3d64d37e-3a9d-4ff6-aeb7-7e70828fe463","is_oa":true,"landing_page_url":"https://research.monash.edu/en/publications/3d64d37e-3a9d-4ff6-aeb7-7e70828fe463","pdf_url":null,"source":{"id":"https://openalex.org/S4306402625","display_name":"Monash University Research Portal (Monash University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I56590836","host_organization_name":"Monash University","host_organization_lineage":["https://openalex.org/I56590836"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Tan, C W, Herrmann, M, Salehi, M & Webb, G I 2025, 'Proximity forest 2.0 : a new effective and scalable similarity-based classifier for time series', Data Mining and Knowledge Discovery, vol. 39, no. 2, 14. https://doi.org/10.1007/s10618-024-01085-0","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1007/s10618-024-01085-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-024-01085-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-024-01085-0.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5950738133","display_name":null,"funder_award_id":"DP210100072","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"}],"funders":[{"id":"https://openalex.org/F4320320971","display_name":"Monash University","ror":"https://ror.org/02bfwt286"},{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4407574880.pdf"},"referenced_works_count":56,"referenced_works":["https://openalex.org/W58346954","https://openalex.org/W1534304300","https://openalex.org/W1968354112","https://openalex.org/W1970013651","https://openalex.org/W2008348094","https://openalex.org/W2050493487","https://openalex.org/W2091921805","https://openalex.org/W2098759488","https://openalex.org/W2099302229","https://openalex.org/W2118371392","https://openalex.org/W2118529802","https://openalex.org/W2143325592","https://openalex.org/W2551393996","https://openalex.org/W2555077524","https://openalex.org/W2752432857","https://openalex.org/W2765753848","https://openalex.org/W2792071264","https://openalex.org/W2797178690","https://openalex.org/W2802183958","https://openalex.org/W2888791883","https://openalex.org/W2892035503","https://openalex.org/W2939865913","https://openalex.org/W2946507061","https://openalex.org/W2954112873","https://openalex.org/W2967988901","https://openalex.org/W2970478682","https://openalex.org/W2972810968","https://openalex.org/W2982438846","https://openalex.org/W2986524475","https://openalex.org/W2988244882","https://openalex.org/W3010158807","https://openalex.org/W3029579534","https://openalex.org/W3035125997","https://openalex.org/W3042807565","https://openalex.org/W3080921724","https://openalex.org/W3083891030","https://openalex.org/W3098967488","https://openalex.org/W3112330479","https://openalex.org/W3115948762","https://openalex.org/W3186145246","https://openalex.org/W3190461479","https://openalex.org/W3194337021","https://openalex.org/W3202364339","https://openalex.org/W4206952570","https://openalex.org/W4239867545","https://openalex.org/W4241186228","https://openalex.org/W4243296258","https://openalex.org/W4285302383","https://openalex.org/W4315925651","https://openalex.org/W4316499318","https://openalex.org/W4320728679","https://openalex.org/W4328021819","https://openalex.org/W4365999477","https://openalex.org/W4376956372","https://openalex.org/W4386861026","https://openalex.org/W6601010799"],"related_works":["https://openalex.org/W2389214306","https://openalex.org/W2965083567","https://openalex.org/W4235240664","https://openalex.org/W1838576100","https://openalex.org/W2095886385","https://openalex.org/W2889616422","https://openalex.org/W2119012848","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"Abstract":[0],"Time":[1,139],"series":[2,115],"classification":[3,23],"(TSC)":[4],"is":[5],"a":[6,69,132,188,195],"challenging":[7],"task":[8],"due":[9],"to":[10,126,174],"the":[11,86,98,159,166,171,178,211],"diversity":[12],"of":[13,15,40,161,170],"types":[14],"features":[16],"that":[17,100],"may":[18],"be":[19],"relevant":[20],"for":[21,61],"different":[22],"tasks,":[24],"including":[25],"trends,":[26],"variance,":[27],"frequency,":[28],"magnitude,":[29],"and":[30,49,89,124,152,176,203],"various":[31],"patterns.":[32],"To":[33],"address":[34],"this":[35,65],"challenge,":[36],"several":[37],"alternative":[38],"classes":[39],"approach":[41],"have":[42,199],"been":[43],"developed.":[44],"While":[45],"kernel,":[46],"neural":[47],"network,":[48],"hybrid":[50],"approaches":[51,57],"perform":[52],"well":[53],"overall,":[54],"some":[55],"specialized":[56],"are":[58,101],"better":[59],"suited":[60],"specific":[62,95],"tasks.":[63],"In":[64],"paper,":[66],"we":[67],"propose":[68],"new":[70,133],"similarity-based":[71,83],"classifier,":[72],"Proximity":[73],"Forest":[74],"version":[75],"2.0":[76,108,206],"(PF":[77],"2.0),":[78],"which":[79],"outperforms":[80,90],"previous":[81],"state-of-the-art":[82,92],"classifiers":[84],"across":[85],"UCR":[87],"benchmark":[88,99],"other":[91],"methods":[93],"on":[94],"datasets":[96],"in":[97,113,208],"best":[102],"addressed":[103],"by":[104],"similarity-base":[105],"methods.":[106],"PF":[107,173,201,205,212],"incorporates":[109,193],"three":[110],"recent":[111],"advances":[112],"time":[114],"similarity":[116,129,135,162,184],"measures":[117,163,169],"\u2014":[118],"(1)":[119],"computationally":[120],"efficient":[121],"early":[122],"abandoning":[123],"pruning":[125],"speedup":[127],"elastic":[128,134],"computations;":[130],"(2)":[131],"measure,":[136],"Amerced":[137],"Dynamic":[138],"Warping":[140],"(":[141],"$${{\\,\\textrm{ADTW}\\,}}$$":[142],"<mml:math":[143],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\">":[144],"<mml:mrow>":[145],"<mml:mspace/>":[146,148],"<mml:mtext>ADTW</mml:mtext>":[147],"</mml:mrow>":[149],"</mml:math>":[150],");":[151],"(3)":[153],"cost":[154],"function":[155],"tuning.":[156],"It":[157,191],"rationalizes":[158],"set":[160],"employed,":[164],"reducing":[165],"eight":[167],"base":[168],"original":[172],"four":[175],"using":[177],"first":[179],"derivative":[180],"transform":[181],"with":[182],"all":[183],"measures,":[185],"rather":[186],"than":[187],"limited":[189],"subset.":[190],"also":[192],"HYDRA,":[194],"dictionary-based":[196],"transform.":[197],"We":[198],"re-implemented":[200],"1.0":[202],"implemented":[204],"framework":[207,213],"Java,":[209],"making":[210],"more":[214],"efficient.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3}],"updated_date":"2026-06-13T06:13:01.061226","created_date":"2025-10-10T00:00:00"}
