{"id":"https://openalex.org/W4406216728","doi":"https://doi.org/10.3390/sym17010090","title":"Effective Text Classification Through Supervised Rough Set-Based Term Weighting","display_name":"Effective Text Classification Through Supervised Rough Set-Based Term Weighting","publication_year":2025,"publication_date":"2025-01-09","ids":{"openalex":"https://openalex.org/W4406216728","doi":"https://doi.org/10.3390/sym17010090"},"language":"en","primary_location":{"id":"doi:10.3390/sym17010090","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17010090","pdf_url":"https://www.mdpi.com/2073-8994/17/1/90/pdf?version=1736391698","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2073-8994/17/1/90/pdf?version=1736391698","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054093788","display_name":"Rasim \u00c7ek\u0456k","orcid":"https://orcid.org/0000-0002-7820-413X"},"institutions":[{"id":"https://openalex.org/I240986617","display_name":"\u015e\u0131rnak University","ror":"https://ror.org/01fcvkv23","country_code":"TR","type":"education","lineage":["https://openalex.org/I240986617"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Ras\u0131m \u00c7ekik","raw_affiliation_strings":["Department of Computer Engineering, Faculty of Engineering, \u015e\u0131rnak University, 73000 \u015e\u0131rnak, Turkey"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Faculty of Engineering, \u015e\u0131rnak University, 73000 \u015e\u0131rnak, Turkey","institution_ids":["https://openalex.org/I240986617"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5054093788"],"corresponding_institution_ids":["https://openalex.org/I240986617"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":4.3105,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.93093492,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"17","issue":"1","first_page":"90","last_page":"90"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.9937999844551086,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9861999750137329,"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/weighting","display_name":"Weighting","score":0.827176570892334},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6675052642822266},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.6172165870666504},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5893441438674927},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5671752095222473},{"id":"https://openalex.org/keywords/multiclass-classification","display_name":"Multiclass classification","score":0.5407495498657227},{"id":"https://openalex.org/keywords/rough-set","display_name":"Rough set","score":0.520885705947876},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.5191634893417358},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5124290585517883},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4832097291946411},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4761876165866852},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.44448965787887573},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44143998622894287},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3881617784500122}],"concepts":[{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.827176570892334},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6675052642822266},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.6172165870666504},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5893441438674927},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5671752095222473},{"id":"https://openalex.org/C123860398","wikidata":"https://www.wikidata.org/wiki/Q6934605","display_name":"Multiclass classification","level":3,"score":0.5407495498657227},{"id":"https://openalex.org/C111012933","wikidata":"https://www.wikidata.org/wiki/Q3137210","display_name":"Rough set","level":2,"score":0.520885705947876},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.5191634893417358},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5124290585517883},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4832097291946411},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4761876165866852},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.44448965787887573},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44143998622894287},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3881617784500122},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/sym17010090","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17010090","pdf_url":"https://www.mdpi.com/2073-8994/17/1/90/pdf?version=1736391698","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8cf440c48ac247a78b1b9a93d4110a7f","is_oa":true,"landing_page_url":"https://doaj.org/article/8cf440c48ac247a78b1b9a93d4110a7f","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry, Vol 17, Iss 1, p 90 (2025)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2073-8994/17/1/90/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/sym17010090","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"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":"Symmetry","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/sym17010090","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17010090","pdf_url":"https://www.mdpi.com/2073-8994/17/1/90/pdf?version=1736391698","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.5400000214576721,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4406216728.pdf","grobid_xml":"https://content.openalex.org/works/W4406216728.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W1595907733","https://openalex.org/W1980891397","https://openalex.org/W2000950075","https://openalex.org/W2033028868","https://openalex.org/W2034998024","https://openalex.org/W2077563243","https://openalex.org/W2105948726","https://openalex.org/W2112119668","https://openalex.org/W2134090438","https://openalex.org/W2140321362","https://openalex.org/W2153291472","https://openalex.org/W2395763878","https://openalex.org/W2493916176","https://openalex.org/W2520488855","https://openalex.org/W2561539065","https://openalex.org/W2574441945","https://openalex.org/W2937124885","https://openalex.org/W2962739339","https://openalex.org/W2973226110","https://openalex.org/W3034912391","https://openalex.org/W3040895835","https://openalex.org/W3090556797","https://openalex.org/W3156338075","https://openalex.org/W4213009331","https://openalex.org/W4220853643","https://openalex.org/W4223477121","https://openalex.org/W4317521107","https://openalex.org/W4390467884","https://openalex.org/W6683971644","https://openalex.org/W6788247690"],"related_works":["https://openalex.org/W2388318061","https://openalex.org/W2039745824","https://openalex.org/W3157224608","https://openalex.org/W2747895175","https://openalex.org/W4376528628","https://openalex.org/W2614669534","https://openalex.org/W2623163150","https://openalex.org/W2186666570","https://openalex.org/W2123014508","https://openalex.org/W2104406636"],"abstract_inverted_index":{"This":[0,14,103],"research":[1],"presents":[2,113],"an":[3,98],"innovative":[4],"approach":[5],"in":[6,33,70],"text":[7,58,252],"mining":[8],"based":[9],"on":[10,56,167,200],"rough":[11,23,129],"set":[12,24],"theory.":[13],"study":[15,104],"fundamentally":[16],"utilizes":[17],"the":[18,86,91,106,117,136,151,154,180,216,223,232,236,246,251],"concept":[19],"of":[20,50,94,153,213],"symmetry":[21],"from":[22],"theory":[25],"to":[26,131,178,221],"construct":[27],"indiscernibility":[28],"matrices":[29],"and":[30,40,48,112,139,147,174,188,202,211,228],"model":[31,127],"uncertainties":[32],"data":[34,53],"analysis,":[35],"ensuring":[36],"both":[37],"methodological":[38],"structure":[39],"solution":[41],"processes":[42],"remain":[43],"symmetric.":[44],"The":[45,125,183],"effective":[46,88,225],"management":[47],"analysis":[49],"large-scale":[51],"textual":[52],"heavily":[54],"relies":[55],"automated":[57],"classification":[59,72,253],"technologies.":[60],"In":[61],"this":[62],"context,":[63],"term":[64,76,155],"weighting":[65,77],"plays":[66],"a":[67,141],"crucial":[68],"role":[69],"determining":[71],"performance.":[73,254],"Particularly,":[74],"supervised":[75],"methods":[78,192,248],"that":[79,186,245],"utilize":[80],"class":[81,176,209],"information":[82],"have":[83],"emerged":[84],"as":[85],"most":[87,224],"approaches.":[89],"However,":[90],"optimal":[92],"representation":[93],"class\u2013term":[95],"relationships":[96],"remains":[97],"area":[99],"requiring":[100],"further":[101],"research.":[102],"proposes":[105],"Rough":[107,120],"Multivariate":[108,121],"Weighting":[109,122],"Scheme":[110,123],"(RMWS)":[111],"its":[114,189],"mathematical":[115],"derivative,":[116],"Square":[118],"Root":[119],"(SRMWS).":[124],"RMWS":[126,187,233],"employs":[128],"sets":[130],"identify":[132],"information-carrying":[133],"documents":[134],"within":[135],"document\u2013term\u2013class":[137],"space":[138],"adopts":[140],"computational":[142],"methodology":[143],"incorporating":[144],"\u03b1,":[145],"\u03b2,":[146],"\u03b3":[148],"coefficients.":[149],"Moreover,":[150],"distribution":[152],"among":[156],"classes":[157],"is":[158,219],"again":[159],"effectively":[160],"revealed.":[161],"Comprehensive":[162],"experimental":[163],"studies":[164],"were":[165],"conducted":[166],"three":[168],"different":[169],"datasets":[170,204],"featuring":[171],"imbalanced-multiclass,":[172],"balanced-multiclass,":[173],"imbalanced-binary":[175],"structures":[177],"evaluate":[179],"model\u2019s":[181],"effectiveness.":[182],"results":[184,238,243],"show":[185,244],"derivative":[190],"SRMWS":[191,217],"outperform":[193],"existing":[194],"approaches":[195],"by":[196,208],"exhibiting":[197],"superior":[198],"performance":[199],"balanced":[201],"unbalanced":[203],"without":[205],"being":[206],"affected":[207],"imbalance":[210],"number":[212],"classes.":[214],"Furthermore,":[215],"method":[218,234],"found":[220],"be":[222],"for":[226,239],"SVM":[227],"KNN":[229],"classifiers,":[230],"while":[231],"achieves":[235],"best":[237],"NB":[240],"classifiers.":[241],"These":[242],"proposed":[247],"significantly":[249],"improve":[250]},"counts_by_year":[{"year":2026,"cited_by_count":3}],"updated_date":"2026-04-07T14:57:38.498316","created_date":"2025-10-10T00:00:00"}
