{"id":"https://openalex.org/W2093672547","doi":"https://doi.org/10.1109/wacv.2015.65","title":"Sequential Boosting for Learning a Random Forest Classifier","display_name":"Sequential Boosting for Learning a Random Forest Classifier","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W2093672547","doi":"https://doi.org/10.1109/wacv.2015.65","mag":"2093672547"},"language":"en","primary_location":{"id":"doi:10.1109/wacv.2015.65","doi":"https://doi.org/10.1109/wacv.2015.65","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2015.65","pdf_url":null,"source":{"id":"https://openalex.org/S4393918690","display_name":"IEEE Winter Conference on Applications of Computer Vision","issn_l":"2472-6737","issn":["2472-6737"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Winter Conference on Applications of Computer Vision","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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":null,"display_name":"Florian Baumann","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I114112103","display_name":"Leibniz University Hannover","ror":"https://ror.org/0304hq317","country_code":"DE","type":"education","lineage":["https://openalex.org/I114112103"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Florian Baumann","raw_affiliation_strings":["Institut f\u00fcr Informationsverarbeitung (TNT), Leibniz Universit\u00e4t Hannover, Hannover, Germany","Inst. f\u03c5r Informationsverarbeitung (TNT), Leibniz Univ. Hannover, Hannover, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institut f\u00fcr Informationsverarbeitung (TNT), Leibniz Universit\u00e4t Hannover, Hannover, Germany","institution_ids":["https://openalex.org/I114112103"]},{"raw_affiliation_string":"Inst. f\u03c5r Informationsverarbeitung (TNT), Leibniz Univ. Hannover, Hannover, Germany","institution_ids":["https://openalex.org/I114112103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043650942","display_name":"Arne Ehlers","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I114112103","display_name":"Leibniz University Hannover","ror":"https://ror.org/0304hq317","country_code":"DE","type":"education","lineage":["https://openalex.org/I114112103"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Arne Ehlers","raw_affiliation_strings":["Institut f\u00fcr Informationsverarbeitung (TNT), Leibniz Universit\u00e4t Hannover, Hannover, Germany","Inst. f\u03c5r Informationsverarbeitung (TNT), Leibniz Univ. Hannover, Hannover, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institut f\u00fcr Informationsverarbeitung (TNT), Leibniz Universit\u00e4t Hannover, Hannover, Germany","institution_ids":["https://openalex.org/I114112103"]},{"raw_affiliation_string":"Inst. f\u03c5r Informationsverarbeitung (TNT), Leibniz Univ. Hannover, Hannover, Germany","institution_ids":["https://openalex.org/I114112103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040412734","display_name":"Bodo Rosenhahn","orcid":"https://orcid.org/0000-0003-3861-1424","observed_orcids":["https://orcid.org/0000-0003-3861-1424"]},"institutions":[{"id":"https://openalex.org/I114112103","display_name":"Leibniz University Hannover","ror":"https://ror.org/0304hq317","country_code":"DE","type":"education","lineage":["https://openalex.org/I114112103"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bodo Rosenhahn","raw_affiliation_strings":["Institut f\u00fcr Informationsverarbeitung (TNT), Leibniz Universit\u00e4t Hannover, Hannover, Germany","Inst. f\u03c5r Informationsverarbeitung (TNT), Leibniz Univ. Hannover, Hannover, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institut f\u00fcr Informationsverarbeitung (TNT), Leibniz Universit\u00e4t Hannover, Hannover, Germany","institution_ids":["https://openalex.org/I114112103"]},{"raw_affiliation_string":"Inst. f\u03c5r Informationsverarbeitung (TNT), Leibniz Univ. Hannover, Hannover, Germany","institution_ids":["https://openalex.org/I114112103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100641142","display_name":"Wei Liu","orcid":"https://orcid.org/0000-0002-7409-0948","observed_orcids":["https://orcid.org/0000-0002-7409-0948","https://orcid.org/0000-0001-8398-8648","https://orcid.org/0000-0002-0330-8073","https://orcid.org/0000-0001-6351-9019","https://orcid.org/0000-0002-0623-7178","https://orcid.org/0000-0002-5388-9080","https://orcid.org/0000-0002-6450-1969","https://orcid.org/0009-0001-6324-3830","https://orcid.org/0000-0002-1679-6862","https://orcid.org/0000-0003-3070-3340","https://orcid.org/0009-0009-1128-2916"]},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Liu","raw_affiliation_strings":["Department of Automation, USTC, Hefei, Anhui, P.R. China","Dept. of Autom., USTC, Hefei, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, USTC, Hefei, Anhui, P.R. China","institution_ids":["https://openalex.org/I126520041"]},{"raw_affiliation_string":"Dept. of Autom., USTC, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.07363843,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":"1","issue":null,"first_page":"442","last_page":"447"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.42410001158714294,"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"}},"topics":[{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.42410001158714294,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.41190001368522644,"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/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.019300000742077827,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.7979999780654907},{"id":"https://openalex.org/keywords/tree-induction","display_name":"tree induction","score":0.9409999847412109},{"id":"https://openalex.org/keywords/weighted-majority-voting","display_name":"weighted majority voting","score":0.9089999794960022},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"ensemble learning","score":0.6299999952316284},{"id":"https://openalex.org/keywords/object-recognition","display_name":"object recognition","score":0.9409999847412109},{"id":"https://openalex.org/keywords/human-action-recognition","display_name":"human action recognition","score":0.996999979019165}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.9128502607345581},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7939262986183167},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7203855514526367},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6457885503768921},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5959131121635437},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.575431227684021},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.548244833946228},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4696468710899353},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.45412367582321167},{"id":"https://openalex.org/C153668964","wikidata":"https://www.wikidata.org/wiki/Q27636","display_name":"Majority rule","level":2,"score":0.4408693313598633},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.4373651146888733},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1826111078262329},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv.2015.65","doi":"https://doi.org/10.1109/wacv.2015.65","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2015.65","pdf_url":null,"source":{"id":"https://openalex.org/S4393918690","display_name":"IEEE Winter Conference on Applications of Computer Vision","issn_l":"2472-6737","issn":["2472-6737"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Winter Conference on Applications of Computer Vision","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W954291123","https://openalex.org/W1480376833","https://openalex.org/W1487602158","https://openalex.org/W1930624869","https://openalex.org/W1974614303","https://openalex.org/W1985188514","https://openalex.org/W2010399676","https://openalex.org/W2013076218","https://openalex.org/W2024502389","https://openalex.org/W2034328688","https://openalex.org/W2042346250","https://openalex.org/W2067713319","https://openalex.org/W2090834590","https://openalex.org/W2096691069","https://openalex.org/W2100659887","https://openalex.org/W2106670658","https://openalex.org/W2107285841","https://openalex.org/W2112076978","https://openalex.org/W2112796928","https://openalex.org/W2113242816","https://openalex.org/W2120240539","https://openalex.org/W2130313024","https://openalex.org/W2136853139","https://openalex.org/W2142194269","https://openalex.org/W2146634731","https://openalex.org/W2152556536","https://openalex.org/W2160207817","https://openalex.org/W2166020076","https://openalex.org/W2396672726","https://openalex.org/W2547599103","https://openalex.org/W2911964244","https://openalex.org/W2912934387","https://openalex.org/W3120740533","https://openalex.org/W4249279051","https://openalex.org/W4308164799","https://openalex.org/W4313313956"],"related_works":["https://openalex.org/W1974614303","https://openalex.org/W2563441803","https://openalex.org/W31594615","https://openalex.org/W2188759683","https://openalex.org/W3097533296","https://openalex.org/W4392948943","https://openalex.org/W1583814341","https://openalex.org/W4388687207","https://openalex.org/W4407002074","https://openalex.org/W2904378110"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"a":[3,19,32,73,80],"novel":[4],"tree":[5,60,83],"induction":[6],"algorithm":[7,97],"called":[8],"sequential":[9],"Random":[10,21],"Forest":[11,22],"(sRF)":[12],"to":[13,47,79,103],"improve":[14],"the":[15,28,38,50,63,67,89],"detection":[16],"accuracy":[17],"of":[18,31,40,52,91],"standard":[20,113],"classifier.":[23],"Observations":[24],"have":[25],"shown":[26],"that":[27,58],"overall":[29],"performance":[30],"forest":[33],"is":[34,46],"strongly":[35],"influenced":[36],"by":[37],"number":[39,51],"training":[41,53],"samples.":[42],"The":[43,95],"main":[44],"idea":[45],"sequentially":[48],"adapt":[49],"samples":[54],"per":[55],"class":[56,81],"so":[57],"each":[59],"better":[61],"complements":[62],"existing":[64],"trees":[65],"in":[66,101],"whole":[68],"forest.":[69],"Further,":[70],"we":[71],"propose":[72],"weighted":[74],"majority":[75],"voting":[76],"with":[77],"respect":[78],"and":[82,117],"specific":[84],"error":[85],"rate":[86],"for":[87,109,120],"decreasing":[88],"influence":[90],"poorly":[92],"performing":[93],"trees.":[94],"sRF":[96],"shows":[98],"competing":[99],"results":[100],"comparison":[102],"state-of-the-art":[104],"approaches":[105],"using":[106],"two":[107,112],"datasets":[108,116,119],"object":[110],"recognition,":[111],"machine":[114],"learning":[115],"three":[118],"human":[121],"action":[122],"recognition.":[123]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2016-06-24T00:00:00"}