{"id":"https://openalex.org/W3097336633","doi":"https://doi.org/10.1007/s00500-020-05270-3","title":"Pruning trees in C-fuzzy random forest","display_name":"Pruning trees in C-fuzzy random forest","publication_year":2020,"publication_date":"2020-10-28","ids":{"openalex":"https://openalex.org/W3097336633","doi":"https://doi.org/10.1007/s00500-020-05270-3","mag":"3097336633"},"language":"en","primary_location":{"id":"doi:10.1007/s00500-020-05270-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00500-020-05270-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00500-020-05270-3.pdf","source":{"id":"https://openalex.org/S65753830","display_name":"Soft Computing","issn_l":"1432-7643","issn":["1432-7643","1433-7479"],"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":"Soft Computing","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/s00500-020-05270-3.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063978223","display_name":"\u0141ukasz Gadomer","orcid":null},"institutions":[{"id":"https://openalex.org/I1323121030","display_name":"Bialystok University of Technology","ror":"https://ror.org/02bzfsy61","country_code":"PL","type":"education","lineage":["https://openalex.org/I1323121030"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"\u0141ukasz Gadomer","raw_affiliation_strings":["Faculty of Computer Science, Bialystok University of Technology, Bia\u0142ystok, Poland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science, Bialystok University of Technology, Bia\u0142ystok, Poland","institution_ids":["https://openalex.org/I1323121030"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056729537","display_name":"Zenon A. Sosnowski","orcid":"https://orcid.org/0000-0002-6911-5944"},"institutions":[{"id":"https://openalex.org/I1323121030","display_name":"Bialystok University of Technology","ror":"https://ror.org/02bzfsy61","country_code":"PL","type":"education","lineage":["https://openalex.org/I1323121030"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Zenon A. Sosnowski","raw_affiliation_strings":["Faculty of Computer Science, Bialystok University of Technology, Bia\u0142ystok, Poland"],"raw_orcid":"https://orcid.org/0000-0002-6911-5944","affiliations":[{"raw_affiliation_string":"Faculty of Computer Science, Bialystok University of Technology, Bia\u0142ystok, Poland","institution_ids":["https://openalex.org/I1323121030"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5056729537"],"corresponding_institution_ids":["https://openalex.org/I1323121030"],"apc_list":{"value":2990,"currency":"USD","value_usd":2990},"apc_paid":{"value":2990,"currency":"USD","value_usd":2990},"fwci":0.3245,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.64325853,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"25","issue":"3","first_page":"1995","last_page":"2013"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10050","display_name":"Multi-Criteria Decision Making","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10050","display_name":"Multi-Criteria Decision Making","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9722999930381775,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.968999981880188,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/pruning","display_name":"Pruning","score":0.7658885717391968},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.6875962018966675},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.5621368288993835},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5149827599525452},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5077406167984009},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4906577169895172},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47179192304611206},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4663243591785431},{"id":"https://openalex.org/keywords/incremental-decision-tree","display_name":"Incremental decision tree","score":0.4438297152519226},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42167043685913086},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.41653311252593994},{"id":"https://openalex.org/keywords/decision-tree-learning","display_name":"Decision tree learning","score":0.3791433274745941},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08512276411056519},{"id":"https://openalex.org/keywords/botany","display_name":"Botany","score":0.047708213329315186}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7658885717391968},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6875962018966675},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.5621368288993835},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5149827599525452},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5077406167984009},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4906577169895172},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47179192304611206},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4663243591785431},{"id":"https://openalex.org/C10229987","wikidata":"https://www.wikidata.org/wiki/Q17083028","display_name":"Incremental decision tree","level":4,"score":0.4438297152519226},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42167043685913086},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.41653311252593994},{"id":"https://openalex.org/C5481197","wikidata":"https://www.wikidata.org/wiki/Q16766476","display_name":"Decision tree learning","level":3,"score":0.3791433274745941},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08512276411056519},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.047708213329315186},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s00500-020-05270-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00500-020-05270-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00500-020-05270-3.pdf","source":{"id":"https://openalex.org/S65753830","display_name":"Soft Computing","issn_l":"1432-7643","issn":["1432-7643","1433-7479"],"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":"Soft Computing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s00500-020-05270-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00500-020-05270-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00500-020-05270-3.pdf","source":{"id":"https://openalex.org/S65753830","display_name":"Soft Computing","issn_l":"1432-7643","issn":["1432-7643","1433-7479"],"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":"Soft Computing","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7099999785423279,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[{"id":"https://openalex.org/G5167435011","display_name":null,"funder_award_id":"S/WI/3/2018","funder_id":"https://openalex.org/F4320322733","funder_display_name":"Ministerstwo Edukacji i Nauki"},{"id":"https://openalex.org/G6598436080","display_name":null,"funder_award_id":"S/WI/3/2018.","funder_id":"https://openalex.org/F4320323075","funder_display_name":"Politechnika Bialostocka"}],"funders":[{"id":"https://openalex.org/F4320322733","display_name":"Ministerstwo Edukacji i Nauki","ror":"https://ror.org/05dwvd537"},{"id":"https://openalex.org/F4320323075","display_name":"Politechnika Bialostocka","ror":"https://ror.org/02bzfsy61"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3097336633.pdf","grobid_xml":"https://content.openalex.org/works/W3097336633.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W1602363634","https://openalex.org/W1604329830","https://openalex.org/W1979441003","https://openalex.org/W2045470243","https://openalex.org/W2060605237","https://openalex.org/W2079944366","https://openalex.org/W2103478177","https://openalex.org/W2111022379","https://openalex.org/W2113076747","https://openalex.org/W2128420091","https://openalex.org/W2330820318","https://openalex.org/W2510118262","https://openalex.org/W2577217074","https://openalex.org/W2616253993","https://openalex.org/W2789885537","https://openalex.org/W2952226757","https://openalex.org/W2972273766","https://openalex.org/W2979539276","https://openalex.org/W3005210259","https://openalex.org/W3085162807"],"related_works":["https://openalex.org/W1982169401","https://openalex.org/W2591672004","https://openalex.org/W2030894524","https://openalex.org/W1648970942","https://openalex.org/W4243803609","https://openalex.org/W4319437832","https://openalex.org/W4387869645","https://openalex.org/W2348694184","https://openalex.org/W957478802","https://openalex.org/W2356463514"],"abstract_inverted_index":{"Abstract":[0],"Pruning":[1,98,102,106,110],"decision":[2,37,42,74,79,142,148,153,160,168,174,216,223],"trees":[3,38,43,75,93,120,122,190,239,284],"is":[4,48,53,65,252,280],"the":[5,28,82,205,240,245,258,271],"way":[6],"to":[7,13,35,89,179,270,282],"decrease":[8],"their":[9],"size":[10],"in":[11,44,191,195],"order":[12],"reduce":[14,197],"classification":[15,23,202,207],"time":[16,199],"and":[17,39,59,71,94,112,121,150,171,200,219,244],"improve":[18,201],"(or":[19],"at":[20],"least":[21],"maintain)":[22],"accuracy.":[24,203],"In":[25],"this":[26],"paper,":[27],"idea":[29],"of":[30,92,126,134],"applying":[31],"different":[32,166,230],"pruning":[33,85,128,183,189,231,254,263],"methods":[34,86,129,232],"C-fuzzy":[36,45,50,73,115,147,192],"Cluster\u2013context":[40,77,158],"fuzzy":[41,68,78,159],"random":[46,51,69,116,193],"forest":[47,52,70,194],"presented.":[49],"a":[54],"classifier":[55],"which":[56,237,256],"we":[57,60],"created":[58,135],"are":[61,275],"improving.":[62],"This":[63],"solution":[64],"based":[66],"on":[67,81,139,164],"uses":[72],"or":[76],"trees\u2014depending":[80],"variant.":[83],"Five":[84],"were":[87,130,177],"adjusted":[88],"mentioned":[90],"kind":[91],"examined:":[95],"Reduced":[96],"Error":[97,101,105],"(REP),":[99],"Pessimistic":[100],"(PEP),":[103],"Minimum":[104],"(MEP),":[107],"Critical":[108],"Value":[109],"(CVP)":[111],"Cost-Complexity":[113],"Pruning.":[114],"forests":[117,136],"with":[118,146,157],"unpruned":[119],"constructed":[123],"using":[124,212],"each":[125,228],"these":[127],"created.":[131],"The":[132,162,235],"evaluation":[133],"was":[137,210,242,248],"performed":[138,178],"eleven":[140,165],"discrete":[141,167,215],"class":[143,154,169,175,217,224],"datasets":[144,155,170,176],"(forest":[145,156],"trees)":[149],"two":[151,172],"continuous":[152,173,222],"trees).":[161],"experiments":[163,186],"evaluate":[180],"five":[181],"implemented":[182],"methods.":[184],"Our":[185],"show":[187],"that":[188],"general":[196],"computation":[198],"Generalizing,":[204],"best":[206,259],"accuracy":[208],"improvement":[209],"achieved":[211],"CVP":[213],"for":[214,221,227,260],"problems":[218],"REP":[220],"datasets,":[225],"but":[226],"dataset":[229],"work":[233],"well.":[234],"method":[236,255,264],"pruned":[238],"most":[241],"PEP":[243],"fastest":[246],"one":[247],"MEP.":[249],"However,":[250],"there":[251],"no":[253],"fits":[257],"all":[261],"datasets\u2014the":[262],"should":[265],"be":[266],"chosen":[267],"individually":[268],"according":[269],"given":[272],"problem.":[273],"There":[274],"also":[276],"situations":[277],"where":[278],"it":[279],"better":[281],"remain":[283],"unpruned.":[285]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}
