{"id":"https://openalex.org/W2744561409","doi":"https://doi.org/10.1109/inista.2017.8001125","title":"Unsupervised feature selection using reversed correlation for improved medical diagnosis","display_name":"Unsupervised feature selection using reversed correlation for improved medical diagnosis","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2744561409","doi":"https://doi.org/10.1109/inista.2017.8001125","mag":"2744561409"},"language":"en","primary_location":{"id":"doi:10.1109/inista.2017.8001125","is_oa":false,"landing_page_url":"https://doi.org/10.1109/inista.2017.8001125","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 INnovations in Intelligent SysTems and Applications (INISTA)","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/A5057494227","display_name":"Agnieszka Wosiak","orcid":"https://orcid.org/0000-0001-6124-1236"},"institutions":[{"id":"https://openalex.org/I188884621","display_name":"Lodz University of Technology","ror":"https://ror.org/00s8fpf52","country_code":"PL","type":"education","lineage":["https://openalex.org/I188884621"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Agnieszka Wosiak","raw_affiliation_strings":["Lodz University of Technology, Institute of Information Technology, L\u00f3dz, Poland"],"affiliations":[{"raw_affiliation_string":"Lodz University of Technology, Institute of Information Technology, L\u00f3dz, Poland","institution_ids":["https://openalex.org/I188884621"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080782572","display_name":"Danuta Zakrzewska","orcid":"https://orcid.org/0000-0002-1546-4823"},"institutions":[{"id":"https://openalex.org/I188884621","display_name":"Lodz University of Technology","ror":"https://ror.org/00s8fpf52","country_code":"PL","type":"education","lineage":["https://openalex.org/I188884621"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Danuta Zakrzewska","raw_affiliation_strings":["Lodz University of Technology, Institute of Information Technology, L\u00f3dz, Poland"],"affiliations":[{"raw_affiliation_string":"Lodz University of Technology, Institute of Information Technology, L\u00f3dz, Poland","institution_ids":["https://openalex.org/I188884621"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5057494227"],"corresponding_institution_ids":["https://openalex.org/I188884621"],"apc_list":null,"apc_paid":null,"fwci":1.4471,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.82767928,"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12647","display_name":"Traditional Chinese Medicine Studies","score":0.986299991607666,"subfield":{"id":"https://openalex.org/subfields/2707","display_name":"Complementary and alternative medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T12647","display_name":"Traditional Chinese Medicine Studies","score":0.986299991607666,"subfield":{"id":"https://openalex.org/subfields/2707","display_name":"Complementary and alternative medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9846000075340271,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9484000205993652,"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/feature-selection","display_name":"Feature selection","score":0.6826761960983276},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6455551385879517},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6201072931289673},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.6000871658325195},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5358511209487915},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.510630190372467},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4464511275291443},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35181504487991333},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3308505415916443},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19287064671516418}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6826761960983276},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6455551385879517},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6201072931289673},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.6000871658325195},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5358511209487915},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.510630190372467},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4464511275291443},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35181504487991333},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3308505415916443},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19287064671516418},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"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/inista.2017.8001125","is_oa":false,"landing_page_url":"https://doi.org/10.1109/inista.2017.8001125","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 INnovations in Intelligent SysTems and Applications (INISTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1495061682","https://openalex.org/W1548056638","https://openalex.org/W1570448133","https://openalex.org/W1594924988","https://openalex.org/W1598874232","https://openalex.org/W1894159108","https://openalex.org/W1991147747","https://openalex.org/W2049192244","https://openalex.org/W2052122702","https://openalex.org/W2056834354","https://openalex.org/W2113586398","https://openalex.org/W2126185804","https://openalex.org/W2133750711","https://openalex.org/W2140190241","https://openalex.org/W2389088196","https://openalex.org/W2543247009","https://openalex.org/W2616792955","https://openalex.org/W4213279169","https://openalex.org/W4244381840","https://openalex.org/W4285719527","https://openalex.org/W6629652716","https://openalex.org/W6710734855"],"related_works":["https://openalex.org/W4205762803","https://openalex.org/W2535856026","https://openalex.org/W2265065644","https://openalex.org/W2134699697","https://openalex.org/W3017188156","https://openalex.org/W2322875716","https://openalex.org/W2961085424","https://openalex.org/W3147584709","https://openalex.org/W4386564352","https://openalex.org/W2952668426"],"abstract_inverted_index":{"Statistical":[0],"inference":[1,101],"has":[2,77],"been":[3,78],"usually":[4],"used":[5],"for":[6,30,46],"medical":[7,104],"data":[8,85],"analysis,":[9,58],"however":[10],"in":[11,64],"many":[12],"cases":[13],"it":[14],"appears":[15],"not":[16],"to":[17,53,103],"be":[18,35],"efficient":[19],"enough.":[20],"Cluster":[21],"analysis":[22],"enables":[23],"finding":[24,47],"out":[25],"groups":[26],"of":[27,56,71,87,99],"similar":[28],"instances,":[29],"which":[31,50],"statistical":[32,57,100],"models":[33],"can":[34],"built":[36],"more":[37],"effectively.":[38],"In":[39],"the":[40,94],"paper":[41],"a":[42],"feature":[43],"selection":[44],"method":[45,62],"clustering":[48],"attributes,":[49],"are":[51],"supposed":[52],"improve":[54],"performance":[55],"is":[59],"proposed.":[60],"The":[61,74],"consists":[63],"selecting":[65],"reversed":[66],"correlated":[67],"features":[68],"as":[69],"attributes":[70],"cluster":[72],"analysis.":[73],"proposed":[75],"technique":[76],"evaluated":[79],"by":[80],"experiments":[81],"done":[82],"on":[83],"real":[84],"sets":[86],"cardiovascular":[88],"cases.":[89],"Experiment":[90],"results":[91],"showed":[92],"that":[93],"presented":[95],"approach":[96],"stimulates":[97],"efficacy":[98],"applied":[102],"diagnosis.":[105]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}