{"id":"https://openalex.org/W2403452903","doi":"https://doi.org/10.1007/978-3-319-25783-9_24","title":"The Boosting and Bootstrap Ensemble for Classifiers Based on Weak Rough Inclusions","display_name":"The Boosting and Bootstrap Ensemble for Classifiers Based on Weak Rough Inclusions","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W2403452903","doi":"https://doi.org/10.1007/978-3-319-25783-9_24","mag":"2403452903"},"language":"en","primary_location":{"id":"doi:10.1007/978-3-319-25783-9_24","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-319-25783-9_24","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"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":"book series"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1007/978-3-319-25783-9_24","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5006908029","display_name":"Piotr Artiemjew","orcid":"https://orcid.org/0000-0001-5508-9856"},"institutions":[{"id":"https://openalex.org/I47996466","display_name":"University of Warmia and Mazury in Olsztyn","ror":"https://ror.org/05s4feg49","country_code":"PL","type":"education","lineage":["https://openalex.org/I47996466"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Piotr Artiemjew","raw_affiliation_strings":["Department of Mathematics and Computer Science, University of Warmia and Mazury, Olsztyn, Poland"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Computer Science, University of Warmia and Mazury, Olsztyn, Poland","institution_ids":["https://openalex.org/I47996466"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5006908029"],"corresponding_institution_ids":["https://openalex.org/I47996466"],"apc_list":{"value":5000,"currency":"EUR","value_usd":5392},"apc_paid":{"value":5000,"currency":"EUR","value_usd":5392},"fwci":1.7254,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.85019175,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"267","last_page":"277"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11727","display_name":"Advanced Algebra and Logic","score":0.9902999997138977,"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/boosting","display_name":"Boosting (machine learning)","score":0.9208672046661377},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7468062043190002},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6666540503501892},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5465403199195862},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5376980900764465},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48191264271736145},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.4396120309829712},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4334685206413269},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.41875749826431274},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13740885257720947},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12274390459060669}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.9208672046661377},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7468062043190002},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6666540503501892},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5465403199195862},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5376980900764465},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48191264271736145},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4396120309829712},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4334685206413269},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.41875749826431274},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13740885257720947},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12274390459060669}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/978-3-319-25783-9_24","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-319-25783-9_24","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"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":"book series"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.1007/978-3-319-25783-9_24","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-319-25783-9_24","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"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":"book series"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.550000011920929}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W26285442","https://openalex.org/W168051455","https://openalex.org/W189898176","https://openalex.org/W1533228521","https://openalex.org/W1984297727","https://openalex.org/W2067885219","https://openalex.org/W2072485548","https://openalex.org/W2110182592","https://openalex.org/W2113662065","https://openalex.org/W2115449743","https://openalex.org/W2116509046","https://openalex.org/W2145073242","https://openalex.org/W2164016421","https://openalex.org/W2168020168","https://openalex.org/W2210682848","https://openalex.org/W2342272644","https://openalex.org/W2396144055","https://openalex.org/W2478117552","https://openalex.org/W4213245422"],"related_works":["https://openalex.org/W3082059448","https://openalex.org/W2905156999","https://openalex.org/W4313640622","https://openalex.org/W4229460275","https://openalex.org/W3092085822","https://openalex.org/W4296079469","https://openalex.org/W1987518466","https://openalex.org/W3023033471","https://openalex.org/W4376643315","https://openalex.org/W3135046080"],"abstract_inverted_index":{"In":[0,63],"the":[1,7,15,32,35,41,47,55,77,98,105,114,129],"recent":[2],"works":[3],"we":[4,66],"have":[5,67,91],"investigated":[6],"classifiers":[8,109],"based":[9,81,122],"on":[10,31,82,94,123],"weak":[11,108],"rough":[12],"inclusions,":[13],"especially":[14,53],"8v1.1":[16],"-":[17],"8v1.5":[18],"algorithms.":[19],"These":[20],"algorithms":[21,112],"in":[22,50,54,113],"process":[23],"of":[24,43,57,59,107,116,118],"weights":[25],"forming":[26],"for":[27,72],"classification":[28,58],"dynamically":[29],"react":[30],"distance":[33],"between":[34],"particular":[36],"attributes.":[37],"Our":[38],"results":[39,102],"show":[40,103],"effectiveness":[42],"these":[44],"methods":[45,71],"and":[46,84],"wide":[48],"application":[49],"many":[51],"contexts,":[52],"context":[56,115],"DNA":[60],"Microarray":[61],"data.":[62],"this":[64],"work":[65],"checked":[68],"a":[69],"few":[70],"classifier":[73],"stabilisation,":[74],"such":[75],"as":[76],"Bootstrap":[78],"Ensemble,":[79],"Boosting":[80,121],"Arcing,":[83],"Ada-Boost":[85],"with":[86],"Monte":[87],"Carlo":[88],"split.":[89],"We":[90],"performed":[92],"experiments":[93],"selected":[95],"data":[96],"from":[97],"UCI":[99],"Repository.":[100],"The":[101,120],"that":[104],"committee":[106],"stabilised":[110],"our":[111],"accuracy":[117],"classification.":[119],"Arcing":[124],"turned":[125],"out":[126],"to":[127],"be":[128],"most":[130],"promising":[131],"method":[132],"among":[133],"those":[134],"examined.":[135]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
