{"id":"https://openalex.org/W3162485060","doi":"https://doi.org/10.1109/icpr48806.2021.9413262","title":"Watermelon: a Novel Feature Selection Method Based on Bayes Error Rate Estimation and a New Interpretation of Feature Relevance and Redundancy","display_name":"Watermelon: a Novel Feature Selection Method Based on Bayes Error Rate Estimation and a New Interpretation of Feature Relevance and Redundancy","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3162485060","doi":"https://doi.org/10.1109/icpr48806.2021.9413262","mag":"3162485060"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9413262","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9413262","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5063333627","display_name":"Xiang Xie","orcid":"https://orcid.org/0000-0002-4277-574X"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Xiang Xie","raw_affiliation_strings":["Institute for Information Processing Technologies, Karlsruhe Institute of Technology, Karlsruhe, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Information Processing Technologies, Karlsruhe Institute of Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067846212","display_name":"Wilhelm Stork","orcid":"https://orcid.org/0000-0003-0579-4615"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Wilhelm Stork","raw_affiliation_strings":["Institute for Information Processing Technologies, Karlsruhe Institute of Technology, Karlsruhe, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Information Processing Technologies, Karlsruhe Institute of Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I102335020"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"7","issue":null,"first_page":"1360","last_page":"1367"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9994999766349792,"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/T10057","display_name":"Face and Expression Recognition","score":0.9994999766349792,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9954000115394592,"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":0.9930999875068665,"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":0.8093595504760742},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.6932774782180786},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6529339551925659},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6275108456611633},{"id":"https://openalex.org/keywords/minimum-redundancy-feature-selection","display_name":"Minimum redundancy feature selection","score":0.6229645609855652},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6154011487960815},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6147717237472534},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.5195101499557495},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.5081236362457275},{"id":"https://openalex.org/keywords/mutual-information","display_name":"Mutual information","score":0.5014183521270752},{"id":"https://openalex.org/keywords/bayes-error-rate","display_name":"Bayes error rate","score":0.4841924011707306},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.4509470462799072},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4411185383796692},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43858394026756287},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4284169673919678},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2304697334766388},{"id":"https://openalex.org/keywords/bayes-classifier","display_name":"Bayes classifier","score":0.18127170205116272},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.17086467146873474}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.8093595504760742},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.6932774782180786},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6529339551925659},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6275108456611633},{"id":"https://openalex.org/C16811321","wikidata":"https://www.wikidata.org/wiki/Q17138905","display_name":"Minimum redundancy feature selection","level":3,"score":0.6229645609855652},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6154011487960815},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6147717237472534},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.5195101499557495},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.5081236362457275},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.5014183521270752},{"id":"https://openalex.org/C143809311","wikidata":"https://www.wikidata.org/wiki/Q4874458","display_name":"Bayes error rate","level":5,"score":0.4841924011707306},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.4509470462799072},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4411185383796692},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43858394026756287},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4284169673919678},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2304697334766388},{"id":"https://openalex.org/C185207860","wikidata":"https://www.wikidata.org/wiki/Q17004744","display_name":"Bayes classifier","level":4,"score":0.18127170205116272},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.17086467146873474},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr48806.2021.9413262","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9413262","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1500895378","https://openalex.org/W1565746575","https://openalex.org/W1580817707","https://openalex.org/W1613448136","https://openalex.org/W1661871015","https://openalex.org/W1871180460","https://openalex.org/W2043772506","https://openalex.org/W2045518901","https://openalex.org/W2054717882","https://openalex.org/W2082610780","https://openalex.org/W2098021754","https://openalex.org/W2101234009","https://openalex.org/W2106525823","https://openalex.org/W2118561568","https://openalex.org/W2121007818","https://openalex.org/W2149454242","https://openalex.org/W2149772057","https://openalex.org/W2154053567","https://openalex.org/W2155344811","https://openalex.org/W2155925556","https://openalex.org/W2156483112","https://openalex.org/W2156504490","https://openalex.org/W2162833336","https://openalex.org/W2162833766","https://openalex.org/W2165432359","https://openalex.org/W2165580920","https://openalex.org/W2171296290","https://openalex.org/W2171837816","https://openalex.org/W2256782633","https://openalex.org/W2799148064","https://openalex.org/W2902209350","https://openalex.org/W2997674406","https://openalex.org/W2998216295","https://openalex.org/W2998768810","https://openalex.org/W3097993951","https://openalex.org/W3105524694","https://openalex.org/W3144510026","https://openalex.org/W4285719527","https://openalex.org/W4289236186","https://openalex.org/W6633774736","https://openalex.org/W6636914306","https://openalex.org/W6675354045","https://openalex.org/W6677853180","https://openalex.org/W6682496738","https://openalex.org/W6682686508","https://openalex.org/W6683024897","https://openalex.org/W6683381688","https://openalex.org/W6684050148","https://openalex.org/W6684173676","https://openalex.org/W7066667914"],"related_works":["https://openalex.org/W2156571267","https://openalex.org/W3120617324","https://openalex.org/W1973600295","https://openalex.org/W2350815964","https://openalex.org/W1838735596","https://openalex.org/W3164528651","https://openalex.org/W3036204000","https://openalex.org/W2042378471","https://openalex.org/W2033333781","https://openalex.org/W2058380590"],"abstract_inverted_index":{"Feature":[0],"selection":[1,30,77,138],"has":[2],"become":[3],"a":[4,27,65],"crucial":[5],"part":[6],"of":[7,18,20,51,59,102],"many":[8,75],"classification":[9,123],"problems":[10],"in":[11,64,140],"which":[12,79],"high-dimensional":[13],"datasets":[14],"may":[15],"contain":[16],"tens":[17],"thousands":[19],"features.":[21],"In":[22],"this":[23],"paper,":[24],"we":[25,47],"propose":[26],"novel":[28],"feature":[29,60,76,137],"method":[31],"scoring":[32],"the":[33,37,49,57,70,126],"features":[34,52,82,96,105],"through":[35],"estimating":[36],"Bayes":[38],"error":[39],"rate":[40],"based":[41,106],"on":[42,107,120],"kernel":[43],"density":[44],"estimation.":[45],"Additionally,":[46],"update":[48],"scores":[50],"dynamically":[53],"by":[54,74],"quantitatively":[55],"interpreting":[56],"effects":[58],"relevance":[61,103],"and":[62,97,112,125],"redundancy":[63],"new":[66],"way.":[67],"Distinguishing":[68],"from":[69],"common":[71],"heuristic":[72],"applied":[73],"methods,":[78],"prefers":[80],"choosing":[81],"that":[83,129],"are":[84],"not":[85],"relevant":[86],"to":[87],"each":[88],"other,":[89],"our":[90,130],"approach":[91,131],"penalizes":[92],"only":[93],"monotonically":[94],"correlated":[95],"rewards":[98],"any":[99],"other":[100,133],"kind":[101],"among":[104],"Spearman's":[108],"rank":[109],"correlation":[110],"coefficient":[111],"normalized":[113],"mutual":[114],"information.":[115],"We":[116],"conduct":[117],"extensive":[118],"experiments":[119],"seventeen":[121,134],"diverse":[122],"benchmarks":[124],"results":[127],"show":[128],"overperforms":[132],"popular":[135],"state-of-the-art":[136],"methods":[139],"most":[141],"cases.":[142]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
