{"id":"https://openalex.org/W2913203924","doi":"https://doi.org/10.1109/thms.2019.2892436","title":"A Machine Learning Approach to Predict Human Judgments in Compensatory and Noncompensatory Judgment Tasks","display_name":"A Machine Learning Approach to Predict Human Judgments in Compensatory and Noncompensatory Judgment Tasks","publication_year":2019,"publication_date":"2019-02-05","ids":{"openalex":"https://openalex.org/W2913203924","doi":"https://doi.org/10.1109/thms.2019.2892436","mag":"2913203924"},"language":"en","primary_location":{"id":"doi:10.1109/thms.2019.2892436","is_oa":false,"landing_page_url":"https://doi.org/10.1109/thms.2019.2892436","pdf_url":null,"source":{"id":"https://openalex.org/S2476799526","display_name":"IEEE Transactions on Human-Machine Systems","issn_l":"2168-2291","issn":["2168-2291","2168-2305"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Human-Machine Systems","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/A5010590881","display_name":"Joseph Nuamah","orcid":"https://orcid.org/0000-0001-7172-0716"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joseph K. Nuamah","raw_affiliation_strings":["Industrial and Systems Engineering Department, Texas A&M University, College Station, TX, USA"],"raw_orcid":"https://orcid.org/0000-0001-7172-0716","affiliations":[{"raw_affiliation_string":"Industrial and Systems Engineering Department, Texas A&M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046775743","display_name":"Younho Seong","orcid":"https://orcid.org/0000-0003-4807-8176"},"institutions":[{"id":"https://openalex.org/I35777872","display_name":"North Carolina Agricultural and Technical State University","ror":"https://ror.org/02aze4h65","country_code":"US","type":"education","lineage":["https://openalex.org/I35777872"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Younho Seong","raw_affiliation_strings":["Industrial and Systems Engineering Department, North Carolina A&T State University, Greensboro, NC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Industrial and Systems Engineering Department, North Carolina A&T State University, Greensboro, NC, USA","institution_ids":["https://openalex.org/I35777872"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1750,"currency":"USD","value_usd":1750},"apc_paid":null,"fwci":0.6726,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.61923688,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"49","issue":"4","first_page":"326","last_page":"336"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10315","display_name":"Decision-Making and Behavioral Economics","score":0.9936000108718872,"subfield":{"id":"https://openalex.org/subfields/1800","display_name":"General Decision Sciences"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10315","display_name":"Decision-Making and Behavioral Economics","score":0.9936000108718872,"subfield":{"id":"https://openalex.org/subfields/1800","display_name":"General Decision Sciences"},"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/T10050","display_name":"Multi-Criteria Decision Making","score":0.9830999970436096,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9690999984741211,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.6955010294914246},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.591818630695343},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5687019228935242},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.5494338274002075},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5414142608642578},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.5012929439544678},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.41932815313339233},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.3573533296585083}],"concepts":[{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6955010294914246},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.591818630695343},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5687019228935242},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5494338274002075},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5414142608642578},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.5012929439544678},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.41932815313339233},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3573533296585083},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/thms.2019.2892436","is_oa":false,"landing_page_url":"https://doi.org/10.1109/thms.2019.2892436","pdf_url":null,"source":{"id":"https://openalex.org/S2476799526","display_name":"IEEE Transactions on Human-Machine Systems","issn_l":"2168-2291","issn":["2168-2291","2168-2305"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Human-Machine Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.8100000023841858}],"awards":[{"id":"https://openalex.org/G899917332","display_name":null,"funder_award_id":"FA8750-15-2-0116","funder_id":"https://openalex.org/F4320338294","funder_display_name":"Air Force Research Laboratory"}],"funders":[{"id":"https://openalex.org/F4320338294","display_name":"Air Force Research Laboratory","ror":"https://ror.org/02e2egq70"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W88324309","https://openalex.org/W144804269","https://openalex.org/W178169899","https://openalex.org/W429766147","https://openalex.org/W585141030","https://openalex.org/W1504694836","https://openalex.org/W1680797894","https://openalex.org/W1731049107","https://openalex.org/W1779897838","https://openalex.org/W1881800483","https://openalex.org/W1964224171","https://openalex.org/W1977482026","https://openalex.org/W1988631338","https://openalex.org/W1990364927","https://openalex.org/W1996246244","https://openalex.org/W2009787395","https://openalex.org/W2011978464","https://openalex.org/W2023095215","https://openalex.org/W2026087162","https://openalex.org/W2045494090","https://openalex.org/W2046411892","https://openalex.org/W2050288270","https://openalex.org/W2058705056","https://openalex.org/W2062000381","https://openalex.org/W2084335597","https://openalex.org/W2104505043","https://openalex.org/W2121001250","https://openalex.org/W2125055259","https://openalex.org/W2132210580","https://openalex.org/W2134433022","https://openalex.org/W2135423934","https://openalex.org/W2140381874","https://openalex.org/W2149511912","https://openalex.org/W2156049655","https://openalex.org/W2159071801","https://openalex.org/W2170498360","https://openalex.org/W2263529863","https://openalex.org/W2293054526","https://openalex.org/W2419657716","https://openalex.org/W2498119267","https://openalex.org/W2507495592","https://openalex.org/W2508215302","https://openalex.org/W2510620627","https://openalex.org/W2531051184","https://openalex.org/W2582743722","https://openalex.org/W2583868060","https://openalex.org/W2615227392","https://openalex.org/W2618132268","https://openalex.org/W2665282810","https://openalex.org/W2740728143","https://openalex.org/W2911964244","https://openalex.org/W2914019270","https://openalex.org/W3194470864","https://openalex.org/W4210960099","https://openalex.org/W4241692252","https://openalex.org/W4249804345","https://openalex.org/W4254207636","https://openalex.org/W4320300817","https://openalex.org/W6603651955","https://openalex.org/W6605938287"],"related_works":["https://openalex.org/W2180954594","https://openalex.org/W2052835778","https://openalex.org/W2049003611","https://openalex.org/W2127804977","https://openalex.org/W2108418243","https://openalex.org/W164103134","https://openalex.org/W2040545019","https://openalex.org/W2787352659","https://openalex.org/W1970611213","https://openalex.org/W4206560911"],"abstract_inverted_index":{"Traditionally,":[0],"in":[1,40,68,88,107,134],"judgment":[2,70,92,138],"analysis,":[3],"multiple":[4,142],"linear":[5,35,102,141],"regression":[6,36,104],"based":[7],"lens":[8,77],"model,":[9],"which":[10],"assumes":[11],"decision":[12,158],"makers":[13],"assess":[14],"every":[15],"cue,":[16],"weigh,":[17],"and":[18,30,54,79,90,110,136,157],"combine":[19],"them":[20],"to":[21,28,62,84],"make":[22],"overall":[23],"judgments,":[24],"has":[25],"been":[26],"used":[27],"model":[29,78,106],"analyze":[31],"human":[32,86,132],"judgments.":[33],"However,":[34],"assumptions":[37],"are":[38,48],"limited":[39],"situations":[41],"where":[42],"logical":[43],"rules":[44],"for":[45,120,155],"making":[46],"decisions":[47],"not":[49],"consistent":[50],"with":[51],"a":[52],"weighting":[53],"summing":[55],"formula.":[56],"In":[57],"this":[58],"study,":[59],"we":[60],"sought":[61],"extend":[63],"the":[64,69,75,95,101,121],"body":[65],"of":[66],"knowledge":[67],"analysis":[71],"research":[72],"by":[73],"adopting":[74],"rule-based":[76],"using":[80],"machine":[81,97,124,148],"learning":[82,98,125,149],"models":[83,99,126,154],"predict":[85],"judgments":[87,133],"compensatory":[89,109,135],"noncompensatory":[91,111,137],"tasks.":[93,112],"Overall,":[94],"selected":[96],"outperformed":[100],"logistic":[103],"(LgR)":[105],"both":[108],"Our":[113],"own":[114],"results":[115],"suggest":[116],"that,":[117],"at":[118,130],"least":[119],"present":[122],"application,":[123],"may":[127],"be":[128],"better":[129],"predicting":[131],"tasks":[139],"than":[140],"LgR":[143],"models.":[144],"We":[145],"conclude":[146],"that":[147],"algorithms":[150],"can":[151],"yield":[152],"useful":[153],"training":[156],"support":[159],"applications.":[160]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2026-08-28T12:50:07.497085","created_date":"2025-10-10T00:00:00"}
