{"id":"https://openalex.org/W4385568291","doi":"https://doi.org/10.1145/3580305.3599781","title":"Binary Classifier Evaluation on Unlabeled Segments using Inverse Distance Weighting with Distance Learning","display_name":"Binary Classifier Evaluation on Unlabeled Segments using Inverse Distance Weighting with Distance Learning","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385568291","doi":"https://doi.org/10.1145/3580305.3599781"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599781","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1145/3580305.3599781","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599781","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599781","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100385771","display_name":"Xu Chen","orcid":"https://orcid.org/0000-0003-3422-0127"},"institutions":[{"id":"https://openalex.org/I3197470489","display_name":"Alpha Omega Alpha Medical Honor Society","ror":"https://ror.org/057q9nn35","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I3197470489"]},{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xu Chen","raw_affiliation_strings":["Meta, Menlo Park, USA"],"affiliations":[{"raw_affiliation_string":"Meta, Menlo Park, USA","institution_ids":["https://openalex.org/I3197470489","https://openalex.org/I4210099336"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018278769","display_name":"Katerina Marazopoulou","orcid":"https://orcid.org/0000-0002-0509-4224"},"institutions":[{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]},{"id":"https://openalex.org/I3197470489","display_name":"Alpha Omega Alpha Medical Honor Society","ror":"https://ror.org/057q9nn35","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I3197470489"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Katerina Marazopoulou","raw_affiliation_strings":["Meta, Menlo Park, USA"],"affiliations":[{"raw_affiliation_string":"Meta, Menlo Park, USA","institution_ids":["https://openalex.org/I3197470489","https://openalex.org/I4210099336"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026923747","display_name":"W.S. Lee","orcid":"https://orcid.org/0009-0002-6903-1043"},"institutions":[{"id":"https://openalex.org/I3197470489","display_name":"Alpha Omega Alpha Medical Honor Society","ror":"https://ror.org/057q9nn35","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I3197470489"]},{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wesley Lee","raw_affiliation_strings":["Meta, Menlo Park, USA"],"affiliations":[{"raw_affiliation_string":"Meta, Menlo Park, USA","institution_ids":["https://openalex.org/I3197470489","https://openalex.org/I4210099336"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102757020","display_name":"Christine Agarwal","orcid":null},"institutions":[{"id":"https://openalex.org/I3197470489","display_name":"Alpha Omega Alpha Medical Honor Society","ror":"https://ror.org/057q9nn35","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I3197470489"]},{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christine Agarwal","raw_affiliation_strings":["Meta, Menlo Park, USA"],"affiliations":[{"raw_affiliation_string":"Meta, Menlo Park, USA","institution_ids":["https://openalex.org/I3197470489","https://openalex.org/I4210099336"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085232740","display_name":"Jason Sukumaran","orcid":"https://orcid.org/0009-0007-1045-4267"},"institutions":[{"id":"https://openalex.org/I3197470489","display_name":"Alpha Omega Alpha Medical Honor Society","ror":"https://ror.org/057q9nn35","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I3197470489"]},{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jason Sukumaran","raw_affiliation_strings":["Meta, Menlo Park, USA"],"affiliations":[{"raw_affiliation_string":"Meta, Menlo Park, USA","institution_ids":["https://openalex.org/I3197470489","https://openalex.org/I4210099336"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014473141","display_name":"Aude Hofleitner","orcid":"https://orcid.org/0009-0002-6492-2488"},"institutions":[{"id":"https://openalex.org/I3197470489","display_name":"Alpha Omega Alpha Medical Honor Society","ror":"https://ror.org/057q9nn35","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I3197470489"]},{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aude Hofleitner","raw_affiliation_strings":["Meta, Menlo Park, USA"],"affiliations":[{"raw_affiliation_string":"Meta, Menlo Park, USA","institution_ids":["https://openalex.org/I3197470489","https://openalex.org/I4210099336"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100385771"],"corresponding_institution_ids":["https://openalex.org/I3197470489","https://openalex.org/I4210099336"],"apc_list":null,"apc_paid":null,"fwci":0.3675,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.7137282,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"10","issue":null,"first_page":"3877","last_page":"3888"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.978600025177002,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.978600025177002,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9483000040054321,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9276000261306763,"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":0.8031148314476013},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7713922262191772},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7709802389144897},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.637352466583252},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.5425856709480286},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5420329570770264},{"id":"https://openalex.org/keywords/usability","display_name":"Usability","score":0.5041788816452026},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4577987492084503},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4484904408454895},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.43687260150909424},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4317641258239746},{"id":"https://openalex.org/keywords/a-weighting","display_name":"A-weighting","score":0.4107816815376282},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16595801711082458},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.08575320243835449}],"concepts":[{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.8031148314476013},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7713922262191772},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7709802389144897},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.637352466583252},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.5425856709480286},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5420329570770264},{"id":"https://openalex.org/C170130773","wikidata":"https://www.wikidata.org/wiki/Q216378","display_name":"Usability","level":2,"score":0.5041788816452026},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4577987492084503},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4484904408454895},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.43687260150909424},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4317641258239746},{"id":"https://openalex.org/C70136482","wikidata":"https://www.wikidata.org/wiki/Q13583781","display_name":"A-weighting","level":3,"score":0.4107816815376282},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16595801711082458},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.08575320243835449},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","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},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580305.3599781","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1145/3580305.3599781","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599781","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3580305.3599781","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1145/3580305.3599781","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599781","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5899999737739563}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385568291.pdf","grobid_xml":"https://content.openalex.org/works/W4385568291.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W189742998","https://openalex.org/W1831050183","https://openalex.org/W1999601178","https://openalex.org/W2003938839","https://openalex.org/W2032536435","https://openalex.org/W2034368206","https://openalex.org/W2099596680","https://openalex.org/W2110086534","https://openalex.org/W2135046866","https://openalex.org/W2147169375","https://openalex.org/W2150291618","https://openalex.org/W2152825437","https://openalex.org/W2925837647","https://openalex.org/W2949762319","https://openalex.org/W3007501395","https://openalex.org/W3098559293","https://openalex.org/W4301028290","https://openalex.org/W6684642658"],"related_works":["https://openalex.org/W2092002118","https://openalex.org/W1880952682","https://openalex.org/W1963516324","https://openalex.org/W2014269659","https://openalex.org/W1971836599","https://openalex.org/W2019002543","https://openalex.org/W2619302272","https://openalex.org/W2180954594","https://openalex.org/W2075728349","https://openalex.org/W2033210010"],"abstract_inverted_index":{"Binary":[0],"classification":[1],"models":[2,21],"are":[3,29,87,97],"ubiquitous,":[4],"and":[5,32,49,123,153,171,192],"reliably":[6],"measuring":[7],"their":[8,13],"performance":[9,18,51,75,101,131,143],"is":[10,22,92],"critical":[11],"for":[12,40],"proper":[14],"usage.":[15],"Ideally,":[16],"the":[17,35,44,58,74,83,100,103,130,133,142,159,177,183,189,195],"of":[19,34,43,57,76,82,102,132,141,179,194],"supervised":[20],"measured":[23],"using":[24],"high-quality":[25],"labeled":[26,145],"datasets":[27],"that":[28,93,128,158],"sufficiently":[30],"large":[31],"representative":[33],"population.":[36],"However,":[37],"obtaining":[38],"labels":[39,86],"all":[41],"segments":[42,56,81,96,108,137],"population":[45,59,84],"can":[46],"be":[47,111],"difficult,":[48],"model":[50,127,134],"typically":[52],"varies":[53],"across":[54],"different":[55,62],"(e.g.,":[60],"in":[61,80,105,135,144,167],"countries).":[63],"In":[64],"this":[65],"work,":[66],"we":[67,114,156],"present":[68],"a":[69,77,116,125,139],"novel":[70],"methodology":[71],"to":[72,118,187],"estimate":[73],"binary":[78],"classifier":[79,104],"where":[85],"unavailable.":[88],"The":[89],"main":[90],"idea":[91],"if":[94],"two":[95,107],"\"similar,''":[98],"then":[99],"these":[106],"would":[109],"also":[110,175],"\"similar.''":[112],"Specifically,":[113],"define":[115],"way":[117],"measure":[119],"similarity":[120],"between":[121],"segments,":[122],"propose":[124],"statistical":[126],"describes":[129],"unlabeled":[136],"as":[138],"function":[140],"segments.":[146],"With":[147],"extensive":[148],"numerical":[149],"experiments":[150],"on":[151,182],"synthetic":[152],"real-world":[154],"datasets,":[155],"demonstrate":[157],"proposed":[160],"method":[161,181],"substantially":[162],"improves":[163],"over":[164],"existing":[165],"methods":[166],"both":[168],"estimation":[169],"accuracy":[170],"computational":[172],"efficiency.":[173],"We":[174],"showcase":[176],"application":[178],"our":[180],"Instagram":[184],"Adult":[185],"Classifier":[186],"improve":[188],"geographic":[190],"coverage":[191],"usability":[193],"model.":[196]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}
