{"id":"https://openalex.org/W1965664138","doi":"https://doi.org/10.4018/ijrsda.2014010106","title":"Comparing and Contrasting Rough Set with Logistic Regression for a Dataset","display_name":"Comparing and Contrasting Rough Set with Logistic Regression for a Dataset","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W1965664138","doi":"https://doi.org/10.4018/ijrsda.2014010106","mag":"1965664138"},"language":"en","primary_location":{"id":"doi:10.4018/ijrsda.2014010106","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijrsda.2014010106","pdf_url":null,"source":{"id":"https://openalex.org/S4210215979","display_name":"International Journal of Rough Sets and Data Analysis","issn_l":"2334-4598","issn":["2334-4598","2334-4601"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Rough Sets and Data Analysis","raw_type":"journal-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/A5011306841","display_name":"Renu Vashist","orcid":null},"institutions":[{"id":"https://openalex.org/I59179055","display_name":"Shri Mata Vaishno Devi University","ror":"https://ror.org/036x6w630","country_code":"IN","type":"education","lineage":["https://openalex.org/I59179055"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Renu Vashist","raw_affiliation_strings":["Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India","Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India","institution_ids":["https://openalex.org/I59179055"]},{"raw_affiliation_string":"Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India#TAB#","institution_ids":["https://openalex.org/I59179055"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076756090","display_name":"Manohar L. Garg","orcid":"https://orcid.org/0000-0003-0514-0865"},"institutions":[{"id":"https://openalex.org/I59179055","display_name":"Shri Mata Vaishno Devi University","ror":"https://ror.org/036x6w630","country_code":"IN","type":"education","lineage":["https://openalex.org/I59179055"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"M. L. Garg","raw_affiliation_strings":["Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India","Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India","institution_ids":["https://openalex.org/I59179055"]},{"raw_affiliation_string":"Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India#TAB#","institution_ids":["https://openalex.org/I59179055"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1014,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.76814376,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"1","issue":"1","first_page":"81","last_page":"98"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9998999834060669,"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":0.9998999834060669,"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/T10050","display_name":"Multi-Criteria Decision Making","score":0.9667999744415283,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9606000185012817,"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/logistic-regression","display_name":"Logistic regression","score":0.7638918161392212},{"id":"https://openalex.org/keywords/rough-set","display_name":"Rough set","score":0.6818596124649048},{"id":"https://openalex.org/keywords/vagueness","display_name":"Vagueness","score":0.6378757953643799},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.6158319711685181},{"id":"https://openalex.org/keywords/dominance-based-rough-set-approach","display_name":"Dominance-based rough set approach","score":0.5565316081047058},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5309894680976868},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5039200186729431},{"id":"https://openalex.org/keywords/logistic-model-tree","display_name":"Logistic model tree","score":0.4792992174625397},{"id":"https://openalex.org/keywords/logit","display_name":"Logit","score":0.45447736978530884},{"id":"https://openalex.org/keywords/multinomial-logistic-regression","display_name":"Multinomial logistic regression","score":0.4243202209472656},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4151056110858917},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4052482843399048},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3793664872646332},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3717106282711029},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.36637476086616516},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.22419112920761108}],"concepts":[{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.7638918161392212},{"id":"https://openalex.org/C111012933","wikidata":"https://www.wikidata.org/wiki/Q3137210","display_name":"Rough set","level":2,"score":0.6818596124649048},{"id":"https://openalex.org/C2776825360","wikidata":"https://www.wikidata.org/wiki/Q1411921","display_name":"Vagueness","level":3,"score":0.6378757953643799},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.6158319711685181},{"id":"https://openalex.org/C39105242","wikidata":"https://www.wikidata.org/wiki/Q5290286","display_name":"Dominance-based rough set approach","level":3,"score":0.5565316081047058},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5309894680976868},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5039200186729431},{"id":"https://openalex.org/C61722155","wikidata":"https://www.wikidata.org/wiki/Q6667643","display_name":"Logistic model tree","level":3,"score":0.4792992174625397},{"id":"https://openalex.org/C140331021","wikidata":"https://www.wikidata.org/wiki/Q1868104","display_name":"Logit","level":2,"score":0.45447736978530884},{"id":"https://openalex.org/C117568660","wikidata":"https://www.wikidata.org/wiki/Q1650843","display_name":"Multinomial logistic regression","level":2,"score":0.4243202209472656},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4151056110858917},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4052482843399048},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3793664872646332},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3717106282711029},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36637476086616516},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.22419112920761108}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.4018/ijrsda.2014010106","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijrsda.2014010106","pdf_url":null,"source":{"id":"https://openalex.org/S4210215979","display_name":"International Journal of Rough Sets and Data Analysis","issn_l":"2334-4598","issn":["2334-4598","2334-4601"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Rough Sets and Data Analysis","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7200000286102295,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W121880908","https://openalex.org/W174527002","https://openalex.org/W1482209170","https://openalex.org/W1484864026","https://openalex.org/W1488236515","https://openalex.org/W1496195566","https://openalex.org/W1503142213","https://openalex.org/W1539811310","https://openalex.org/W1553701556","https://openalex.org/W1557923305","https://openalex.org/W1567546275","https://openalex.org/W1582971263","https://openalex.org/W1584679449","https://openalex.org/W1600904143","https://openalex.org/W1607225427","https://openalex.org/W1914034758","https://openalex.org/W1931213803","https://openalex.org/W1972725533","https://openalex.org/W1973948212","https://openalex.org/W1986537524","https://openalex.org/W1992612096","https://openalex.org/W1995785406","https://openalex.org/W2016382728","https://openalex.org/W2019254124","https://openalex.org/W2031242907","https://openalex.org/W2037323795","https://openalex.org/W2051429600","https://openalex.org/W2089618153","https://openalex.org/W2090578816","https://openalex.org/W2090948841","https://openalex.org/W2110351804","https://openalex.org/W2116798985","https://openalex.org/W2143040521","https://openalex.org/W2143451122","https://openalex.org/W2147042167","https://openalex.org/W2149509893","https://openalex.org/W2153715466","https://openalex.org/W2485462264","https://openalex.org/W2491671861","https://openalex.org/W2570343052","https://openalex.org/W2799202302","https://openalex.org/W2890832274","https://openalex.org/W2990482619","https://openalex.org/W3120740533","https://openalex.org/W4255833381","https://openalex.org/W4299689471","https://openalex.org/W4300648631","https://openalex.org/W4301062750"],"related_works":["https://openalex.org/W137235230","https://openalex.org/W1985068329","https://openalex.org/W1990286199","https://openalex.org/W44395528","https://openalex.org/W4211027790","https://openalex.org/W4246416652","https://openalex.org/W2027354766","https://openalex.org/W1558746179","https://openalex.org/W3145637190","https://openalex.org/W2146628767"],"abstract_inverted_index":{"Rough":[0,155],"Set":[1],"Theory":[2],"(RST)":[3],"is":[4,23,39,97,118,139,150,165,204,209,231,236],"relatively":[5],"new":[6],"and":[7,20,28,50,58,84,104,146],"powerful":[8,62],"mathematical":[9],"tool":[10,213],"to":[11,90,100,113,141,181,214,220,244],"deal":[12,91,101],"with":[13,18,92,102],"imperfect":[14],"data":[15,17,52,172],"(i.e.":[16],"uncertainty":[19],"vagueness)":[21],"which":[22,117],"primarily":[24],"used":[25,41,67,112,140,151],"for":[26,152],"classification":[27],"decision":[29,123,257],"making":[30,258],"problems.":[31],"On":[32],"the":[33,115,119,143,159,171,175,182,193,216,225,249],"other":[34],"hand,":[35],"Logistic":[36],"regression":[37,60,96,145,188,246],"(Logit)":[38],"mainly":[40],"in":[42,68,122,168,239],"Social":[43],"Sciences":[44],"when":[45],"dependent":[46],"variable":[47],"takes":[48],"limited":[49],"categorical":[51],"value":[53],"ranges.":[54],"However,":[55],"both":[56,108],"RST":[57,81],"Logit":[59,95,144],"are":[61,66,111,179,201],"predictable":[63],"models":[64],"that":[65,166,192,206,232],"wide":[69],"range":[70],"of":[71,154,158,162,170,186,224,228,234,251],"applications":[72],"such":[73],"as":[74,86,218,242,254],"medicine,":[75],"military,":[76],"banking,":[77],"financial":[78],"markets":[79],"etc.":[80],"uses":[82],"approximations":[83],"implications":[85],"two":[87,130,199],"formal":[88],"tools":[89,131],"vagueness":[93],"whereas":[94],"severely":[98],"constrained":[99],"vague":[103],"imprecise":[105],"data.":[106],"Yet,":[107],"these":[109,129,198],"methodologies":[110,200],"classify":[114,215],"object":[116],"key":[120],"issue":[121],"making.":[124],"This":[125,190],"research":[126,230],"paper":[127],"compares":[128],"on":[132],"a":[133,255],"common":[134],"dataset.":[135],"SPSS":[136],"17.0":[137],"software":[138,149],"run":[142],"Rose":[147],"2":[148],"analysis":[153],"Set.":[156],"One":[157,223],"important":[160,226],"finding":[161],"this":[163,229],"comparison":[164],"attributes":[167,195],"core":[169],"set":[173,177,208,241,253],"under":[174],"rough":[176,207,240,252],"approach":[178],"similar":[180],"most":[183],"significant":[184,194],"predictors":[185],"logistic":[187,221,245],"model.":[189],"indicates":[191],"deducted":[196],"by":[197],"similar.":[202],"It":[203],"demonstrated":[205],"much":[210,237],"more":[211],"superior":[212],"objects":[217],"compared":[219,243],"regression.":[222],"outcomes":[227],"degree":[233],"accuracy":[235],"higher":[238],"thereby":[247],"establishing":[248],"supremacy":[250],"better":[256],"tool.":[259]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}