{"id":"https://openalex.org/W2051103466","doi":"https://doi.org/10.1109/icdmw.2012.82","title":"Towards Utility Maximization in Regression","display_name":"Towards Utility Maximization in Regression","publication_year":2012,"publication_date":"2012-12-01","ids":{"openalex":"https://openalex.org/W2051103466","doi":"https://doi.org/10.1109/icdmw.2012.82","mag":"2051103466"},"language":"en","primary_location":{"id":"doi:10.1109/icdmw.2012.82","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw.2012.82","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE 12th International Conference on Data Mining Workshops","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://repositorio.inesctec.pt/handle/123456789/4620","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085639693","display_name":"Rita P. Ribeiro","orcid":"https://orcid.org/0000-0002-6852-8077"},"institutions":[{"id":"https://openalex.org/I182534213","display_name":"Universidade do Porto","ror":"https://ror.org/043pwc612","country_code":"PT","type":"education","lineage":["https://openalex.org/I182534213"]}],"countries":["PT"],"is_corresponding":true,"raw_author_name":"Rita P. Ribeiro","raw_affiliation_strings":["LIAAD, Universidade do Porto, Porto, Portugal","LIAAD, Univ. do Porto, Porto, Portugal"],"affiliations":[{"raw_affiliation_string":"LIAAD, Universidade do Porto, Porto, Portugal","institution_ids":["https://openalex.org/I182534213"]},{"raw_affiliation_string":"LIAAD, Univ. do Porto, Porto, Portugal","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5085639693"],"corresponding_institution_ids":["https://openalex.org/I182534213"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.10705003,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"2","issue":null,"first_page":"179","last_page":"186"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9994000196456909,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9994000196456909,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9973999857902527,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9968000054359436,"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/computer-science","display_name":"Computer science","score":0.7195966839790344},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.648362398147583},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.5973324179649353},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5672187209129333},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5415184497833252},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4940350651741028},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.46069926023483276},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.4546930193901062},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.42627519369125366},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4248933792114258},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.41855764389038086},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.4138125479221344},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38966426253318787},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.1550399661064148},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15005075931549072},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12504762411117554},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11111173033714294}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7195966839790344},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.648362398147583},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5973324179649353},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5672187209129333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5415184497833252},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4940350651741028},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.46069926023483276},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.4546930193901062},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.42627519369125366},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4248933792114258},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.41855764389038086},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.4138125479221344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38966426253318787},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.1550399661064148},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15005075931549072},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12504762411117554},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11111173033714294},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icdmw.2012.82","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw.2012.82","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE 12th International Conference on Data Mining Workshops","raw_type":"proceedings-article"},{"id":"pmh:oai:repositorio.inesctec.pt:123456789/4620","is_oa":true,"landing_page_url":"http://repositorio.inesctec.pt/handle/123456789/4620","pdf_url":"http://repositorio.inesctec.pt/handle/123456789/4620","source":{"id":"https://openalex.org/S4306402433","display_name":"Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"conference object"}],"best_oa_location":{"id":"pmh:oai:repositorio.inesctec.pt:123456789/4620","is_oa":true,"landing_page_url":"http://repositorio.inesctec.pt/handle/123456789/4620","pdf_url":"http://repositorio.inesctec.pt/handle/123456789/4620","source":{"id":"https://openalex.org/S4306402433","display_name":"Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"conference object"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/12","score":0.550000011920929,"display_name":"Responsible consumption and production"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320335322","display_name":"European Regional Development Fund","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2051103466.pdf"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W167016754","https://openalex.org/W273955616","https://openalex.org/W1580791986","https://openalex.org/W1582366873","https://openalex.org/W1973715014","https://openalex.org/W1990994240","https://openalex.org/W2032650770","https://openalex.org/W2046488255","https://openalex.org/W2058732827","https://openalex.org/W2094529927","https://openalex.org/W2116642770","https://openalex.org/W3142193787","https://openalex.org/W4252441533","https://openalex.org/W6606837198","https://openalex.org/W6610017368"],"related_works":["https://openalex.org/W2090624569","https://openalex.org/W2947806671","https://openalex.org/W2390878257","https://openalex.org/W4381329258","https://openalex.org/W2891635047","https://openalex.org/W2090638348","https://openalex.org/W2181148280","https://openalex.org/W4252225730","https://openalex.org/W2368374794","https://openalex.org/W2144260821"],"abstract_inverted_index":{"Utility-based":[0],"learning":[1],"is":[2,82],"a":[3,44,64,71,95,104,115,153,159,164],"key":[4],"technique":[5],"for":[6,88,187],"addressing":[7],"many":[8,49],"real":[9],"world":[10],"data":[11],"mining":[12],"applications,":[13],"where":[14,56,90],"the":[15,21,24,30,61,68,77,91,107,120,127,136,146,171,180,189],"costs/benefits":[16,102],"are":[17,48,58],"not":[18],"uniform":[19],"across":[20],"domain":[22],"of":[23,29,63,70,76,94,106,119,122,148,161,166,173,182,197],"target":[25,168],"variable.":[26],"Still,":[27],"most":[28],"existing":[31],"research":[32],"has":[33],"been":[34],"focused":[35],"on":[36,60,79,152,194],"classification":[37],"problems.":[38],"In":[39],"this":[40,80,195],"paper":[41,81],"we":[42],"address":[43],"related":[45],"problem.":[46],"There":[47],"relevant":[50],"domains":[51],"(e.g.":[52],"ecological,":[53],"meteorological,":[54],"finance)":[55],"decisions":[57],"based":[59],"forecast":[62],"numeric":[65,92],"quantity":[66],"(i.e.":[67],"result":[69],"regression":[72,96,124,142,169],"model).":[73],"The":[74,111],"goal":[75],"work":[78],"to":[83,100],"present":[84],"an":[85],"evaluation":[86,150],"framework":[87],"applications":[89],"outcome":[93],"model":[97],"may":[98],"lead":[99],"different":[101],"as":[103],"consequence":[105],"actions":[108],"it":[109],"entails.":[110],"new":[112],"metric":[113,186],"provides":[114],"more":[116,134],"informed":[117],"estimate":[118],"utility":[121,185],"any":[123],"model,":[125],"given":[126],"application-specific":[128],"preference":[129],"biases,":[130],"and":[131,138,156,175],"hence":[132],"makes":[133],"reliable":[135],"comparison":[137],"selection":[139],"between":[140],"alternative":[141],"models.":[143],"We":[144],"illustrate":[145],"objective":[147],"our":[149,167,183],"methodology":[151],"real-life":[154],"application":[155],"also":[157],"carry":[158],"set":[160],"experiments":[162],"over":[163],"subset":[165],"tasks:":[170],"prediction":[172],"rare":[174],"extreme":[176],"values.":[177],"Results":[178],"show":[179],"effectiveness":[181],"proposed":[184],"identifying":[188],"models":[190],"that":[191],"perform":[192],"better":[193],"type":[196],"applications.":[198]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}