{"id":"https://openalex.org/W2153316700","doi":"https://doi.org/10.1109/cbms.2013.6627784","title":"Cost sensitive adaptive random subspace ensemble for computer-aided nodule detection","display_name":"Cost sensitive adaptive random subspace ensemble for computer-aided nodule detection","publication_year":2013,"publication_date":"2013-06-01","ids":{"openalex":"https://openalex.org/W2153316700","doi":"https://doi.org/10.1109/cbms.2013.6627784","mag":"2153316700"},"language":"en","primary_location":{"id":"doi:10.1109/cbms.2013.6627784","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cbms.2013.6627784","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems","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/A5101986225","display_name":"Peng Cao","orcid":"https://orcid.org/0000-0001-9546-7444"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Peng Cao","raw_affiliation_strings":["Northeastern University, Shenyang, Liaoning, CN"],"affiliations":[{"raw_affiliation_string":"Northeastern University, Shenyang, Liaoning, CN","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054322583","display_name":"Dazhe Zhao","orcid":"https://orcid.org/0000-0001-8410-6002"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dazhe Zhao","raw_affiliation_strings":["Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053745515","display_name":"Osmar R. Za\u0131\u0308ane","orcid":"https://orcid.org/0000-0002-0060-5988"},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Osmar Zaiane","raw_affiliation_strings":["University of Alberta, Edmonton, AB, Canada"],"affiliations":[{"raw_affiliation_string":"University of Alberta, Edmonton, AB, Canada","institution_ids":["https://openalex.org/I154425047"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101986225"],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":null,"apc_paid":null,"fwci":1.9237,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.88812265,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"6","issue":null,"first_page":"173","last_page":"178"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.986299991607666,"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":0.986299991607666,"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/subspace-topology","display_name":"Subspace topology","score":0.8812811374664307},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.8461925983428955},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6656814217567444},{"id":"https://openalex.org/keywords/nodule","display_name":"Nodule (geology)","score":0.6432815790176392},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6140706539154053},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6002682447433472},{"id":"https://openalex.org/keywords/random-subspace-method","display_name":"Random subspace method","score":0.513705849647522},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4832499623298645},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3358106017112732}],"concepts":[{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.8812811374664307},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.8461925983428955},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6656814217567444},{"id":"https://openalex.org/C2776731575","wikidata":"https://www.wikidata.org/wiki/Q2916245","display_name":"Nodule (geology)","level":2,"score":0.6432815790176392},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6140706539154053},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6002682447433472},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.513705849647522},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4832499623298645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3358106017112732},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cbms.2013.6627784","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cbms.2013.6627784","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.8199999928474426,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W83133245","https://openalex.org/W1563938718","https://openalex.org/W1580049798","https://openalex.org/W1591261915","https://openalex.org/W1968009201","https://openalex.org/W1971997700","https://openalex.org/W1992455323","https://openalex.org/W2012355247","https://openalex.org/W2023522838","https://openalex.org/W2038839211","https://openalex.org/W2053724458","https://openalex.org/W2071192161","https://openalex.org/W2106479238","https://openalex.org/W2112915739","https://openalex.org/W2113242816","https://openalex.org/W2118978333","https://openalex.org/W2123927174","https://openalex.org/W2148143831","https://openalex.org/W2166249949","https://openalex.org/W6603308255","https://openalex.org/W6633571703","https://openalex.org/W6634569365","https://openalex.org/W6682141768"],"related_works":["https://openalex.org/W2007593166","https://openalex.org/W2143234973","https://openalex.org/W1603777065","https://openalex.org/W2397292208","https://openalex.org/W4221136059","https://openalex.org/W1548677759","https://openalex.org/W1604447007","https://openalex.org/W1547183467","https://openalex.org/W109131529","https://openalex.org/W2086542911"],"abstract_inverted_index":{"Many":[0],"lung":[1],"nodule":[2,36,69],"computer-aided":[3],"detection":[4],"methods":[5],"have":[6],"been":[7],"proposed":[8,117],"to":[9,42,86,98],"help":[10],"radiologists":[11],"in":[12,21,51,73,119,125],"their":[13],"decision":[14],"making.":[15],"Because":[16],"high":[17],"sensitivity":[18],"is":[19],"essential":[20],"the":[22,33,52,55,58,62,66,74,82,100,103,113,116],"candidate":[23],"identification":[24],"stage,":[25],"there":[26],"are":[27],"countless":[28],"false":[29,47],"positives":[30],"produced":[31],"by":[32],"initial":[34],"suspect":[35],"generation":[37],"process,":[38],"giving":[39],"more":[40],"work":[41],"radiologists.":[43],"The":[44],"difficulty":[45],"of":[46,54,57,68,115,121],"positive":[48],"reduction":[49],"lies":[50],"variation":[53],"appearances":[56],"potential":[59],"nodules,":[60],"and":[61,70,105,123],"imbalance":[63],"distribution":[64],"between":[65],"amount":[67],"non-nodule":[71],"candidates":[72],"dataset.":[75],"To":[76],"solve":[77],"these":[78],"challenges,":[79],"we":[80],"extend":[81],"random":[83],"subspace":[84],"method":[85,118],"a":[87],"novel":[88],"Cost":[89],"Sensitive":[90],"Adaptive":[91],"Random":[92],"Subspace":[93],"ensemble":[94],"(CSARS),":[95],"so":[96],"as":[97],"increase":[99],"diversity":[101],"among":[102],"components":[104],"overcome":[106],"imbalanced":[107],"data":[108],"classification.":[109],"Experimental":[110],"results":[111],"show":[112],"effectiveness":[114],"terms":[120],"G-mean":[122],"AUC":[124],"comparison":[126],"with":[127],"commonly":[128],"used":[129],"methods.":[130]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}