{"id":"https://openalex.org/W2988813907","doi":"https://doi.org/10.1109/igarss.2019.8897985","title":"Impact of Non-Proportional Training Sampling of Imbalanced Classes on Land Cover Classification Accuracy with See5 Decision Tree","display_name":"Impact of Non-Proportional Training Sampling of Imbalanced Classes on Land Cover Classification Accuracy with See5 Decision Tree","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2988813907","doi":"https://doi.org/10.1109/igarss.2019.8897985","mag":"2988813907"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8897985","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8897985","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","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/A5026250308","display_name":"Zhengwei Yang","orcid":"https://orcid.org/0000-0002-6532-2663"},"institutions":[{"id":"https://openalex.org/I1336096307","display_name":"United States Department of Agriculture","ror":"https://ror.org/01na82s61","country_code":"US","type":"government","lineage":["https://openalex.org/I1336096307"]},{"id":"https://openalex.org/I1287640093","display_name":"National Agricultural Statistics Service","ror":"https://ror.org/04dpymk59","country_code":"US","type":"government","lineage":["https://openalex.org/I1287640093","https://openalex.org/I1336096307"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zhengwei Yang","raw_affiliation_strings":["United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS)"],"affiliations":[{"raw_affiliation_string":"United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS)","institution_ids":["https://openalex.org/I1287640093","https://openalex.org/I1336096307"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058756914","display_name":"Claire G. Boryan","orcid":null},"institutions":[{"id":"https://openalex.org/I1336096307","display_name":"United States Department of Agriculture","ror":"https://ror.org/01na82s61","country_code":"US","type":"government","lineage":["https://openalex.org/I1336096307"]},{"id":"https://openalex.org/I1287640093","display_name":"National Agricultural Statistics Service","ror":"https://ror.org/04dpymk59","country_code":"US","type":"government","lineage":["https://openalex.org/I1287640093","https://openalex.org/I1336096307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Claire G. Boryan","raw_affiliation_strings":["United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS)"],"affiliations":[{"raw_affiliation_string":"United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS)","institution_ids":["https://openalex.org/I1287640093","https://openalex.org/I1336096307"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5026250308"],"corresponding_institution_ids":["https://openalex.org/I1287640093","https://openalex.org/I1336096307"],"apc_list":null,"apc_paid":null,"fwci":0.42,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.71945129,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"9466","last_page":"9469"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.996399998664856,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.996399998664856,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9932000041007996,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9904000163078308,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.7885220050811768},{"id":"https://openalex.org/keywords/cover","display_name":"Cover (algebra)","score":0.6657497882843018},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6090637445449829},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.5522682666778564},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5243437886238098},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5186225771903992},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5117030143737793},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4911458194255829},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4825981855392456},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.43855226039886475},{"id":"https://openalex.org/keywords/forestry","display_name":"Forestry","score":0.42097893357276917},{"id":"https://openalex.org/keywords/decision-tree-learning","display_name":"Decision tree learning","score":0.4149321913719177},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3770739436149597},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3430268168449402},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2307274341583252},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.20262816548347473},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1343788504600525},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12012746930122375}],"concepts":[{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.7885220050811768},{"id":"https://openalex.org/C2780428219","wikidata":"https://www.wikidata.org/wiki/Q16952335","display_name":"Cover (algebra)","level":2,"score":0.6657497882843018},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6090637445449829},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.5522682666778564},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5243437886238098},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5186225771903992},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5117030143737793},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4911458194255829},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4825981855392456},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.43855226039886475},{"id":"https://openalex.org/C97137747","wikidata":"https://www.wikidata.org/wiki/Q38112","display_name":"Forestry","level":1,"score":0.42097893357276917},{"id":"https://openalex.org/C5481197","wikidata":"https://www.wikidata.org/wiki/Q16766476","display_name":"Decision tree learning","level":3,"score":0.4149321913719177},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3770739436149597},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3430268168449402},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2307274341583252},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.20262816548347473},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1343788504600525},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12012746930122375},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","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},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2019.8897985","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8897985","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.6499999761581421,"display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1482289351","https://openalex.org/W1494131642","https://openalex.org/W1941659294","https://openalex.org/W2008085934","https://openalex.org/W2024968541","https://openalex.org/W2104167780","https://openalex.org/W2199321793","https://openalex.org/W2476749580","https://openalex.org/W2614183994","https://openalex.org/W2767106145","https://openalex.org/W6664464457","https://openalex.org/W6687780495","https://openalex.org/W6737563499","https://openalex.org/W6745643699"],"related_works":["https://openalex.org/W2591672004","https://openalex.org/W1982169401","https://openalex.org/W2356463514","https://openalex.org/W4319437832","https://openalex.org/W2592385415","https://openalex.org/W4243803609","https://openalex.org/W2030894524","https://openalex.org/W2350430350","https://openalex.org/W102063058","https://openalex.org/W2006686080"],"abstract_inverted_index":{"The":[0,40,116],"accuracy":[1,57,92,111],"of":[2,20,25,42,94,112,145,148],"a":[3,78,90,95,113,136],"supervised":[4],"classification":[5,32,56,75,86,132,156],"is":[6,15,45],"highly":[7],"dependent":[8],"upon":[9],"the":[10,18,29,35,51,59,67,110,122,131,142,146,149,154],"training":[11,22,52,69,123,137],"samples.":[12],"This":[13],"paper":[14,44],"concerned":[16],"with":[17],"impact":[19],"non-proportional":[21],"data":[23,70],"sampling":[24,53,71],"imbalanced":[26,60,150],"classes":[27,126,151],"on":[28],"land":[30],"cover":[31],"accuracy,":[33,87],"using":[34,77],"See5":[36,79],"decision":[37,80],"tree":[38,81],"classifier.":[39,82],"purpose":[41],"this":[43],"1)":[46],"to":[47,65,107],"examine":[48],"experimentally":[49],"how":[50],"ratio":[54,72,139,144],"affects":[55],"in":[58],"class":[61],"scenario;":[62],"and":[63,103],"2)":[64],"determine":[66],"best":[68,155],"for":[73],"optimal":[74],"performance":[76],"To":[83],"better":[84],"measure":[85,93],"we":[88],"propose":[89],"balanced":[91],"targeted":[96,114],"class,":[97],"which":[98,140],"incorporates":[99],"both":[100],"False":[101,104],"Positive":[102],"Negative":[105],"errors":[106],"truthfully":[108],"reflect":[109],"class.":[115],"study":[117],"result":[118],"indicates":[119],"that":[120],"balancing":[121],"sample":[124,138],"between":[125],"does":[127],"not":[128],"necessarily":[129],"improve":[130],"accuracy.":[133],"Instead,":[134],"selecting":[135],"equals":[141],"actual":[143],"coverages":[147],"will":[152],"yield":[153],"performance.":[157]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}