{"id":"https://openalex.org/W2772166830","doi":"https://doi.org/10.1109/smc.2017.8122802","title":"Synthetic minority image over-sampling technique: How to improve AUC for glioblastoma patient survival prediction","display_name":"Synthetic minority image over-sampling technique: How to improve AUC for glioblastoma patient survival prediction","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2772166830","doi":"https://doi.org/10.1109/smc.2017.8122802","mag":"2772166830"},"language":"en","primary_location":{"id":"doi:10.1109/smc.2017.8122802","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2017.8122802","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 Systems, Man, and Cybernetics (SMC)","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/A5002010460","display_name":"Renhao Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Renhao Liu","raw_affiliation_strings":["Department of Computer Science and Eng'g, Univ. of South Florida, Tampa, FL"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Eng'g, Univ. of South Florida, Tampa, FL","institution_ids":["https://openalex.org/I2613432"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000168449","display_name":"Lawrence Hall","orcid":"https://orcid.org/0000-0002-7898-8456"},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lawrence O. Hall","raw_affiliation_strings":["Department of Computer Science and Eng'g, Univ. of South Florida, Tampa, FL"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Eng'g, Univ. of South Florida, Tampa, FL","institution_ids":["https://openalex.org/I2613432"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019673624","display_name":"Kevin W. Bowyer","orcid":"https://orcid.org/0000-0002-7562-4390"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kevin W. Bowyer","raw_affiliation_strings":["Department of Computer Science and Eng'g, Univ. of Notre Dame, Notre Dame, IN"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Eng'g, Univ. of Notre Dame, Notre Dame, IN","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053211631","display_name":"Dmitry B. Goldgof","orcid":"https://orcid.org/0000-0001-5461-863X"},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dmitry B. Goldgof","raw_affiliation_strings":["Department of Computer Science and Eng'g, Univ. of South Florida, Tampa, FL"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Eng'g, Univ. of South Florida, Tampa, FL","institution_ids":["https://openalex.org/I2613432"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032533490","display_name":"Robert A. Gatenby","orcid":"https://orcid.org/0000-0002-1621-1510"},"institutions":[{"id":"https://openalex.org/I3019308854","display_name":"Moffitt Cancer Center","ror":"https://ror.org/01xf75524","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I3019308854"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Robert Gatenby","raw_affiliation_strings":["Department of Radiology, Moffitt Cancer Center, Tampa, FL"],"affiliations":[{"raw_affiliation_string":"Department of Radiology, Moffitt Cancer Center, Tampa, FL","institution_ids":["https://openalex.org/I3019308854"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011432412","display_name":"Kaoutar Ben Ahmed","orcid":"https://orcid.org/0000-0002-5269-8733"},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kaoutar Ben Ahmed","raw_affiliation_strings":["Department of Computer Science and Eng'g, Univ. of South Florida, Tampa, FL"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Eng'g, Univ. of South Florida, Tampa, FL","institution_ids":["https://openalex.org/I2613432"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5002010460"],"corresponding_institution_ids":["https://openalex.org/I2613432"],"apc_list":null,"apc_paid":null,"fwci":0.9751,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.82700993,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1357","last_page":"1362"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9993000030517578,"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/T10862","display_name":"AI in cancer detection","score":0.9993000030517578,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.998199999332428,"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/T11652","display_name":"Imbalanced Data Classification Techniques","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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7040361166000366},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6594599485397339},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6168442964553833},{"id":"https://openalex.org/keywords/local-binary-patterns","display_name":"Local binary patterns","score":0.6087271571159363},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.5866565108299255},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5798881649971008},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5123404264450073},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.5056912899017334},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47155141830444336},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4608587324619293},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4229191243648529},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37123727798461914},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3022022247314453}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7040361166000366},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6594599485397339},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6168442964553833},{"id":"https://openalex.org/C87335442","wikidata":"https://www.wikidata.org/wiki/Q2494345","display_name":"Local binary patterns","level":4,"score":0.6087271571159363},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.5866565108299255},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5798881649971008},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5123404264450073},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.5056912899017334},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47155141830444336},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4608587324619293},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4229191243648529},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37123727798461914},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3022022247314453},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc.2017.8122802","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2017.8122802","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 Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1914906354","https://openalex.org/W1979111032","https://openalex.org/W1994326354","https://openalex.org/W1995642009","https://openalex.org/W2006223989","https://openalex.org/W2016053056","https://openalex.org/W2033256038","https://openalex.org/W2065261182","https://openalex.org/W2081296990","https://openalex.org/W2097475056","https://openalex.org/W2103004421","https://openalex.org/W2123458540","https://openalex.org/W2126193388","https://openalex.org/W2128739912","https://openalex.org/W2139916508","https://openalex.org/W2148143831","https://openalex.org/W2160815625","https://openalex.org/W2463320487","https://openalex.org/W2478579650","https://openalex.org/W2550838176","https://openalex.org/W2963200485","https://openalex.org/W6651874931","https://openalex.org/W6666984795","https://openalex.org/W6730164494"],"related_works":["https://openalex.org/W2055219403","https://openalex.org/W2020430625","https://openalex.org/W2044065526","https://openalex.org/W2955952267","https://openalex.org/W2076436625","https://openalex.org/W2058306460","https://openalex.org/W2083564146","https://openalex.org/W2542672976","https://openalex.org/W2049095871","https://openalex.org/W2148428954"],"abstract_inverted_index":{"Real-world":[0],"datasets":[1],"are":[2],"often":[3],"imbalanced,":[4],"with":[5,60,154],"an":[6],"important":[7],"class":[8,51,94,121,169],"having":[9],"many":[10],"fewer":[11],"examples":[12,20,41,73,95,122,153],"than":[13,101],"other":[14],"classes.":[15],"In":[16],"medical":[17,131],"data,":[18,31],"normal":[19],"typically":[21,53],"greatly":[22],"outnumber":[23],"disease":[24],"examples.":[25],"A":[26],"classifier":[27],"learned":[28],"from":[29,74,133,147],"imbalanced":[30,187],"will":[32],"tend":[33],"to":[34,83,110,129,184,191],"be":[35],"very":[36],"good":[37],"at":[38,61,96],"the":[39,43,48,62,75,97,102,111,134,148,162,174,178,186],"predicting":[40],"in":[42],"larger":[44,76],"(normal)":[45],"class,":[46],"yet":[47],"smaller":[49],"(disease)":[50],"is":[52,58],"of":[54,113],"more":[55],"interest.":[56],"Imbalance":[57],"dealt":[59],"feature":[63,68,103,194],"vector":[64,104],"level":[65],"(create":[66],"synthetic":[67,151,193],"vectors":[69],"or":[70,78,190],"discard":[71],"some":[72],"class)":[77],"by":[79,125],"assigning":[80],"differential":[81],"costs":[82],"errors.":[84],"Here,":[85],"we":[86],"introduce":[87],"a":[88,155],"novel":[89],"method":[90,107,164],"for":[91],"over-sampling":[92],"minority":[93,120,135,168],"image":[98,152],"level,":[99],"rather":[100],"level.":[105],"Our":[106],"was":[108],"applied":[109],"problem":[112],"Glioblastoma":[114],"patient":[115],"survival":[116],"group":[117],"prediction.":[118],"Synthetic":[119],"were":[123,144],"created":[124],"adding":[126],"Gaussian":[127],"noise":[128],"original":[130,149],"images":[132],"class.":[136],"Uniform":[137],"local":[138],"binary":[139],"patterns":[140],"(LBP)":[141],"histogram":[142],"features":[143],"then":[145],"extracted":[146],"and":[150,172],"random":[156],"forests":[157],"classifier.":[158],"Experimental":[159],"results":[160],"show":[161],"new":[163],"(Image":[165],"SMOTE)":[166],"increased":[167],"predictive":[170],"accuracy":[171],"also":[173],"AUC":[175],"(area":[176],"under":[177],"receiver":[179],"operating":[180],"characteristic":[181],"curve),":[182],"compared":[183],"using":[185],"dataset":[188],"directly":[189],"creating":[192],"vectors.":[195]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}