{"id":"https://openalex.org/W2790529555","doi":"https://doi.org/10.1109/icip.2017.8296492","title":"Ensemble diversity analysis on remote sensing data classification using random forests","display_name":"Ensemble diversity analysis on remote sensing data classification using random forests","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2790529555","doi":"https://doi.org/10.1109/icip.2017.8296492","mag":"2790529555"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2017.8296492","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296492","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5042936748","display_name":"Samia Boukir","orcid":"https://orcid.org/0000-0002-0907-081X"},"institutions":[{"id":"https://openalex.org/I4210160189","display_name":"Institut Polytechnique de Bordeaux","ror":"https://ror.org/054qv7y42","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210160189"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Samia Boukir","raw_affiliation_strings":["Bordeaux INP, Pessac, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bordeaux INP, Pessac, France","institution_ids":["https://openalex.org/I4210160189"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103128762","display_name":"Andrew Mellor","orcid":"https://orcid.org/0000-0002-3392-3261"},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Andrew Mellor","raw_affiliation_strings":["School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, VIC, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I82951845"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7791,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.71637658,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"11","issue":null,"first_page":"1302","last_page":"1306"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9876999855041504,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9674000144004822,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.877548098564148},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.7746921181678772},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6844345927238464},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6486556529998779},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5755628347396851},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5615696310997009},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5332417488098145},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.5078275799751282},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.48600491881370544},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4748047888278961},{"id":"https://openalex.org/keywords/random-subspace-method","display_name":"Random subspace method","score":0.4546118974685669},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4488365054130554},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.41819679737091064}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.877548098564148},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.7746921181678772},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6844345927238464},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6486556529998779},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5755628347396851},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5615696310997009},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5332417488098145},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.5078275799751282},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.48600491881370544},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4748047888278961},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.4546118974685669},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4488365054130554},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.41819679737091064},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2017.8296492","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296492","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","score":0.7099999785423279,"id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W204215449","https://openalex.org/W1516193414","https://openalex.org/W1949910136","https://openalex.org/W1974207438","https://openalex.org/W1975846642","https://openalex.org/W1998979050","https://openalex.org/W2011287807","https://openalex.org/W2016484971","https://openalex.org/W2019778169","https://openalex.org/W2020549709","https://openalex.org/W2063128958","https://openalex.org/W2063875787","https://openalex.org/W2093287877","https://openalex.org/W2100805904","https://openalex.org/W2115629999","https://openalex.org/W2125410201","https://openalex.org/W2132424470","https://openalex.org/W2135293965","https://openalex.org/W2161921946","https://openalex.org/W2167917621","https://openalex.org/W2168046285","https://openalex.org/W2290022773","https://openalex.org/W2341603962","https://openalex.org/W2911964244","https://openalex.org/W6631129480","https://openalex.org/W6675061970","https://openalex.org/W6704211536"],"related_works":["https://openalex.org/W1981866886","https://openalex.org/W2052615004","https://openalex.org/W3080944905","https://openalex.org/W2073883415","https://openalex.org/W2944292463","https://openalex.org/W3014252901","https://openalex.org/W2652414671","https://openalex.org/W2188759683","https://openalex.org/W4317376680","https://openalex.org/W4360777922"],"abstract_inverted_index":{"Ensemble":[0],"classifiers":[1,6,66],"perform":[2],"better":[3,69],"than":[4],"single":[5],"and":[7,34,67,77],"result":[8],"in":[9,64],"reduced":[10],"generalisation":[11],"error.":[12],"Diversity":[13],"across":[14],"ensemble":[15,32,65],"members":[16],"is":[17,47,57],"a":[18,59,78],"key":[19],"factor":[20],"affecting":[21],"classification":[22,35],"performance.":[23],"Here,":[24],"an":[25],"original":[26],"exploration":[27],"of":[28],"the":[29],"relationship":[30],"between":[31],"diversity":[33,63],"performance":[36,71],"applied":[37],"to":[38,80],"large":[39],"area":[40],"remote":[41],"sensing":[42],"classification,":[43],"using":[44],"random":[45],"forests,":[46],"undertaken.":[48],"Results":[49],"demonstrate":[50],"how":[51],"targeting":[52],"lower":[53],"margin":[54],"training":[55],"samples":[56],"both":[58],"strategy":[60],"for":[61,72],"inducing":[62],"achieving":[68],"classifier":[70],"difficult":[73],"or":[74],"rare":[75],"classes,":[76],"way":[79],"reduce":[81],"data":[82],"redundancy.":[83]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
