{"id":"https://openalex.org/W1983773289","doi":"https://doi.org/10.1109/geoinformatics.2013.6626123","title":"Integration of association-rule and decision tree for high resolution image classification","display_name":"Integration of association-rule and decision tree for high resolution image classification","publication_year":2013,"publication_date":"2013-06-01","ids":{"openalex":"https://openalex.org/W1983773289","doi":"https://doi.org/10.1109/geoinformatics.2013.6626123","mag":"1983773289"},"language":"en","primary_location":{"id":"doi:10.1109/geoinformatics.2013.6626123","is_oa":false,"landing_page_url":"https://doi.org/10.1109/geoinformatics.2013.6626123","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 21st International Conference on Geoinformatics","raw_type":"proceedings-article"},"type":"article","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/A5048975428","display_name":"Ziyong Zhou","orcid":"https://orcid.org/0000-0002-4198-6529"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ziyong Zhou","raw_affiliation_strings":["State Key Laboratory of Petroleum Resource and Prospecting, China University of Petroleum, Beijing, China","State Key Lab. of Pet. Resource & Prospecting, China Univ. of Pet., Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Petroleum Resource and Prospecting, China University of Petroleum, Beijing, China","institution_ids":["https://openalex.org/I204553293"]},{"raw_affiliation_string":"State Key Lab. of Pet. Resource & Prospecting, China Univ. of Pet., Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100731922","display_name":"Yang Zhang","orcid":"https://orcid.org/0000-0001-7123-5315"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Zhang","raw_affiliation_strings":["State Key Laboratory of Petroleum Resource and Prospecting, China University of Petroleum, Beijing, China","State Key Lab. of Pet. Resource & Prospecting, China Univ. of Pet., Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Petroleum Resource and Prospecting, China University of Petroleum, Beijing, China","institution_ids":["https://openalex.org/I204553293"]},{"raw_affiliation_string":"State Key Lab. of Pet. Resource & Prospecting, China Univ. of Pet., Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5048975428"],"corresponding_institution_ids":["https://openalex.org/I204553293"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.06153412,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.991100013256073,"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"}},"topics":[{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.991100013256073,"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"}},{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9797000288963318,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9793000221252441,"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/association-rule-learning","display_name":"Association rule learning","score":0.8725441098213196},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.7131066918373108},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6784889698028564},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6349213719367981},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5831544399261475},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5393128395080566},{"id":"https://openalex.org/keywords/decision-rule","display_name":"Decision rule","score":0.5392791628837585},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5180392861366272},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5076853036880493},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4860022962093353},{"id":"https://openalex.org/keywords/association","display_name":"Association (psychology)","score":0.43941986560821533},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.41648489236831665},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33495432138442993},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18340283632278442}],"concepts":[{"id":"https://openalex.org/C193524817","wikidata":"https://www.wikidata.org/wiki/Q386780","display_name":"Association rule learning","level":2,"score":0.8725441098213196},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.7131066918373108},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6784889698028564},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6349213719367981},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5831544399261475},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5393128395080566},{"id":"https://openalex.org/C84839998","wikidata":"https://www.wikidata.org/wiki/Q5249245","display_name":"Decision rule","level":2,"score":0.5392791628837585},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5180392861366272},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5076853036880493},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4860022962093353},{"id":"https://openalex.org/C142853389","wikidata":"https://www.wikidata.org/wiki/Q744778","display_name":"Association (psychology)","level":2,"score":0.43941986560821533},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.41648489236831665},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33495432138442993},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18340283632278442},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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/geoinformatics.2013.6626123","is_oa":false,"landing_page_url":"https://doi.org/10.1109/geoinformatics.2013.6626123","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 21st International Conference on Geoinformatics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.7699999809265137,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1565377632","https://openalex.org/W1986735063","https://openalex.org/W2102645743","https://openalex.org/W2125283600","https://openalex.org/W2153633422","https://openalex.org/W2154642793","https://openalex.org/W2166559705","https://openalex.org/W2482589566","https://openalex.org/W6682837551"],"related_works":["https://openalex.org/W2392697706","https://openalex.org/W2391054147","https://openalex.org/W366033468","https://openalex.org/W1586045918","https://openalex.org/W1536574209","https://openalex.org/W2333063866","https://openalex.org/W4365514290","https://openalex.org/W2955975957","https://openalex.org/W2170530489","https://openalex.org/W1520927778"],"abstract_inverted_index":{"Association":[0],"rule":[1],"is":[2,44,64,78,91,102,111,138],"one":[3],"of":[4,13,40,142,145,156,164,173],"the":[5,35,41,45,88,94,108,153,162,171,174],"most":[6],"important":[7,29],"rules":[8,27,43,59,76,83,96,127],"in":[9,15,128],"nature.":[10],"Each":[11],"type":[12],"object":[14],"a":[16,129,135],"remotely":[17],"sensed":[18],"image":[19,32,72,101,110],"relates":[20],"to":[21,68],"special":[22],"association":[23,26,58,75,126],"rules,":[24,147],"thus":[25],"are":[28,123,132],"features":[30,122],"for":[31,48,80,92,97,104],"classification,":[33],"and":[34,37,61,66,87,119,134,152,167],"mining":[36,81],"rational":[38],"selection":[39],"effective":[42],"key":[46],"issues":[47],"accurate":[49],"classification.":[50,73,98],"In":[51],"this":[52],"paper,":[53],"an":[54,85],"approach":[55],"that":[56],"integrates":[57],"analysis":[60,77],"decision":[62,89,136],"tree":[63,90,137],"presented":[65],"applied":[67],"object-oriented":[69],"high":[70],"resolution":[71],"The":[74,125,158],"adopted":[79],"strong":[82],"from":[84],"image,":[86],"finding":[93],"optimal":[95],"A":[99],"Geoeye-1":[100,109],"used":[103],"experimental":[105],"data.":[106],"Firstly,":[107],"segmented,":[112],"then":[113],"spatial,":[114],"spectral,":[115],"textural,":[116],"color":[117],"space":[118],"band":[120],"ration":[121],"selected.":[124],"training":[130],"set":[131],"mined,":[133],"designed":[139],"with":[140,161],"consideration":[141],"confidence,":[143],"support":[144],"mined":[146],"as":[148],"well":[149],"spectral":[150],"complexity":[151],"generation":[154],"sequence":[155],"rules.":[157],"visual":[159],"comparison":[160],"results":[163],"K-nearest":[165],"neighbors":[166],"accuracy":[168],"estimation":[169],"validate":[170],"effect":[172],"proposed":[175],"approach.":[176]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
