{"id":"https://openalex.org/W2623012606","doi":"https://doi.org/10.1007/s10994-017-5648-2","title":"Simple strategies for semi-supervised feature selection","display_name":"Simple strategies for semi-supervised feature selection","publication_year":2017,"publication_date":"2017-07-17","ids":{"openalex":"https://openalex.org/W2623012606","doi":"https://doi.org/10.1007/s10994-017-5648-2","mag":"2623012606","pmid":"https://pubmed.ncbi.nlm.nih.gov/31983804"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-017-5648-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-017-5648-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-017-5648-2.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10994-017-5648-2.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067205810","display_name":"Konstantinos Sechidis","orcid":"https://orcid.org/0000-0001-6582-7453"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Konstantinos Sechidis","raw_affiliation_strings":["School of Computer Science, University of Manchester, Manchester, M13 9PL UK","School of Computer Science, University of Manchester, Manchester, M13 9PL, UK"],"raw_orcid":"https://orcid.org/0000-0001-6582-7453","affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Manchester, Manchester, M13 9PL UK","institution_ids":["https://openalex.org/I28407311"]},{"raw_affiliation_string":"School of Computer Science, University of Manchester, Manchester, M13 9PL, UK","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091107483","display_name":"Gavin Brown","orcid":"https://orcid.org/0000-0003-2261-9018"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Gavin Brown","raw_affiliation_strings":["School of Computer Science, University of Manchester, Manchester, M13 9PL UK","School of Computer Science, University of Manchester, Manchester, M13 9PL, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Manchester, Manchester, M13 9PL UK","institution_ids":["https://openalex.org/I28407311"]},{"raw_affiliation_string":"School of Computer Science, University of Manchester, Manchester, M13 9PL, UK","institution_ids":["https://openalex.org/I28407311"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5067205810"],"corresponding_institution_ids":["https://openalex.org/I28407311"],"apc_list":{"value":2990,"currency":"USD","value_usd":2990},"apc_paid":{"value":2990,"currency":"USD","value_usd":2990},"fwci":4.3204,"has_fulltext":true,"cited_by_count":46,"citation_normalized_percentile":{"value":0.95142883,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"107","issue":"2","first_page":"357","last_page":"395"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9991999864578247,"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":0.9991999864578247,"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/T10057","display_name":"Face and Expression Recognition","score":0.9976999759674072,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9962999820709229,"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":0.7342389822006226},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6505461931228638},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6472089290618896},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5829237699508667},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.543832004070282},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.5410197973251343},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5127390623092651},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.505429744720459},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.4917502701282501},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.4172220826148987},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.408041775226593},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3564182221889496},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.07159805297851562}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7342389822006226},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6505461931228638},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6472089290618896},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5829237699508667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.543832004070282},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.5410197973251343},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5127390623092651},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.505429744720459},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4917502701282501},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.4172220826148987},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.408041775226593},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3564182221889496},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.07159805297851562},{"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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":5,"locations":[{"id":"doi:10.1007/s10994-017-5648-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-017-5648-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-017-5648-2.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},{"id":"pmid:31983804","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/31983804","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine learning","raw_type":null},{"id":"pmh:oai:pure.atira.dk:openaire/bf6cff2a-638f-499c-9c39-9687a1026404","is_oa":true,"landing_page_url":"https://research.manchester.ac.uk/en/publications/bf6cff2a-638f-499c-9c39-9687a1026404","pdf_url":null,"source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sechidis, K & Brown, G 2017, 'Simple Strategies for Semi-supervised Feature Selection', Machine Learning, vol. 107, no. 0, pp. 357\u2013395. https://doi.org/10.1007/s10994-017-5648-2","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:europepmc.org:5923126","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6954040","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"},{"id":"pmh:oai:pure.atira.dk:publications/bf6cff2a-638f-499c-9c39-9687a1026404","is_oa":true,"landing_page_url":"https://www.research.manchester.ac.uk/portal/en/publications/simple-strategies-for-semisupervised-feature-selection(bf6cff2a-638f-499c-9c39-9687a1026404).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sechidis, K & Brown, G 2017, 'Simple Strategies for Semi-supervised Feature Selection', Machine Learning, vol. 107, no. 0, pp. 357\u2013395. https://doi.org/10.1007/s10994-017-5648-2","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1007/s10994-017-5648-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-017-5648-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-017-5648-2.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1288522079","display_name":"AnyScale Applications","funder_award_id":"EP/L000725/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G3637061823","display_name":"LAMBDA: Learning Algorithms for Modularity in Broad and Deep Architectures","funder_award_id":"EP/N035127/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G4082175093","display_name":"Manchester Centre for Doctoral Training in Computer Science","funder_award_id":"EP/I028099/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G4099171045","display_name":null,"funder_award_id":"EP/N035127/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G4917891107","display_name":null,"funder_award_id":"EP/L000725/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2623012606.pdf","grobid_xml":"https://content.openalex.org/works/W2623012606.grobid-xml"},"referenced_works_count":74,"referenced_works":["https://openalex.org/W12820539","https://openalex.org/W24143670","https://openalex.org/W30853191","https://openalex.org/W62404953","https://openalex.org/W144440563","https://openalex.org/W149574399","https://openalex.org/W175814537","https://openalex.org/W1524326598","https://openalex.org/W1532801683","https://openalex.org/W1565746575","https://openalex.org/W1574877594","https://openalex.org/W1587266360","https://openalex.org/W1600399982","https://openalex.org/W1754185802","https://openalex.org/W1849729440","https://openalex.org/W1922017469","https://openalex.org/W1944151692","https://openalex.org/W2008901952","https://openalex.org/W2014289389","https://openalex.org/W2019288156","https://openalex.org/W2028138594","https://openalex.org/W2037124948","https://openalex.org/W2038290376","https://openalex.org/W2038894244","https://openalex.org/W2069735839","https://openalex.org/W2070562133","https://openalex.org/W2081028792","https://openalex.org/W2099058811","https://openalex.org/W2099111195","https://openalex.org/W2106078714","https://openalex.org/W2107031757","https://openalex.org/W2110316582","https://openalex.org/W2114718442","https://openalex.org/W2123958887","https://openalex.org/W2127883478","https://openalex.org/W2130589815","https://openalex.org/W2154053567","https://openalex.org/W2155344811","https://openalex.org/W2156418042","https://openalex.org/W2156504490","https://openalex.org/W2158492410","https://openalex.org/W2159080219","https://openalex.org/W2160276026","https://openalex.org/W2162248999","https://openalex.org/W2167663973","https://openalex.org/W2296081581","https://openalex.org/W2344681634","https://openalex.org/W2398606097","https://openalex.org/W2478708596","https://openalex.org/W2510447297","https://openalex.org/W2541803860","https://openalex.org/W2550999023","https://openalex.org/W2605006890","https://openalex.org/W2606211438","https://openalex.org/W2607074821","https://openalex.org/W2610184409","https://openalex.org/W2616198738","https://openalex.org/W2729816158","https://openalex.org/W2810952775","https://openalex.org/W2949805214","https://openalex.org/W2951631132","https://openalex.org/W3099830989","https://openalex.org/W4205699531","https://openalex.org/W4300870773","https://openalex.org/W4302423442","https://openalex.org/W4381059450","https://openalex.org/W6668073587","https://openalex.org/W6669607496","https://openalex.org/W6677238145","https://openalex.org/W6679102953","https://openalex.org/W6683548728","https://openalex.org/W6684054079","https://openalex.org/W6721667826","https://openalex.org/W6992441829"],"related_works":["https://openalex.org/W1557094818","https://openalex.org/W2280422768","https://openalex.org/W3143197806","https://openalex.org/W4252555497","https://openalex.org/W3121175838","https://openalex.org/W4226316650","https://openalex.org/W4382934300","https://openalex.org/W2121061354","https://openalex.org/W4285388059","https://openalex.org/W2438464946"],"abstract_inverted_index":{"What":[0],"is":[1],"the":[2,13,45,97,121,145,155],"simplest":[3],"thing":[4],"you":[5],"can":[6,25,133,142],"do":[7],"to":[8,130],"solve":[9],"a":[10],"problem?":[11],"In":[12],"context":[14],"of":[15,96],"semi-supervised":[16],"feature":[17,81,87,147],"selection,":[18,82],"we":[19,24,33,42,74,99,141,152],"tackle":[20],"exactly":[21],"this\u2014how":[22],"much":[23],"gain":[26],"from":[27],"two":[28,101],"simple":[29,128],"classifier-independent":[30],"strategies.":[31],"If":[32],"have":[34,61],"some":[35,40,92],"binary":[36],"labelled":[37,154],"data":[38,47],"and":[39,71,86,137],"unlabelled,":[41],"could":[43],"assume":[44,52],"unlabelled":[46],"are":[48,123],"all":[49,54],"positives,":[50],"or":[51],"them":[53,90],"negatives.":[55],"These":[56],"minimalist,":[57],"seemingly":[58],"naive,":[59],"approaches":[60,129],"not":[62],"previously":[63],"been":[64],"studied":[65],"in":[66,138],"depth.":[67],"However,":[68],"with":[69,91],"theoretical":[70],"empirical":[72],"studies,":[73],"show":[75],"they":[76],"provide":[77],"powerful":[78],"results":[79],"for":[80],"via":[83],"hypothesis":[84],"testing":[85],"ranking.":[88],"Combining":[89],"\u201csoft\u201d":[93],"prior":[94],"knowledge":[95],"domain,":[98],"derive":[100],"novel":[102],"algorithms":[103],"(":[104],"Semi":[105,107],"-JMI,":[106],"-IAMB)":[108],"that":[109,127],"outperform":[110],"significantly":[111],"more":[112],"complex":[113],"competing":[114],"methods,":[115],"showing":[116],"particularly":[117],"good":[118],"performance":[119],"when":[120],"labels":[122],"missing-not-at-random.":[124],"We":[125],"conclude":[126],"this":[131],"problem":[132],"work":[134],"surprisingly":[135],"well,":[136],"many":[139],"situations":[140],"provably":[143],"recover":[144],"exact":[146],"selection":[148],"dynamics,":[149],"as":[150],"if":[151],"had":[153],"entire":[156],"dataset":[157],".":[158]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":4}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}