{"id":"https://openalex.org/W2002828701","doi":"https://doi.org/10.1145/2623330.2623694","title":"Utilizing temporal patterns for estimating uncertainty in interpretable early decision making","display_name":"Utilizing temporal patterns for estimating uncertainty in interpretable early decision making","publication_year":2014,"publication_date":"2014-08-22","ids":{"openalex":"https://openalex.org/W2002828701","doi":"https://doi.org/10.1145/2623330.2623694","mag":"2002828701"},"language":"en","primary_location":{"id":"doi:10.1145/2623330.2623694","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2623330.2623694","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5057905718","display_name":"Mohamed Ghalwash","orcid":"https://orcid.org/0000-0002-3169-4346"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohamed F. Ghalwash","raw_affiliation_strings":["Temple University, Philadelphia, PA, USA","Temple University, Philadelphia, Pa., USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Temple University, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I84392919"]},{"raw_affiliation_string":"Temple University, Philadelphia, Pa., USA","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085497926","display_name":"Vladan Radosavljevi\u0107","orcid":"https://orcid.org/0009-0006-9128-1101"},"institutions":[{"id":"https://openalex.org/I4210134091","display_name":"Yahoo (United States)","ror":"https://ror.org/040dkzz12","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134091"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vladan Radosavljevic","raw_affiliation_strings":["Yahoo Labs, Sunnyvale, CA, USA","Yahoo! Labs., Sunnyvale, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yahoo Labs, Sunnyvale, CA, USA","institution_ids":["https://openalex.org/I4210134091"]},{"raw_affiliation_string":"Yahoo! Labs., Sunnyvale, CA, USA","institution_ids":["https://openalex.org/I4210134091"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044038055","display_name":"Zoran Obradovi\u0107","orcid":"https://orcid.org/0000-0002-2051-0142"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zoran Obradovic","raw_affiliation_strings":["Temple University, Philadelphia, PA, USA","Temple University, Philadelphia, Pa., USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Temple University, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I84392919"]},{"raw_affiliation_string":"Temple University, Philadelphia, Pa., USA","institution_ids":["https://openalex.org/I84392919"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":51,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"402","last_page":"411"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.983299970626831,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9488999843597412,"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/interpretability","display_name":"Interpretability","score":0.9270731210708618},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6995292901992798},{"id":"https://openalex.org/keywords/warning-system","display_name":"Warning system","score":0.6352747678756714},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.572952389717102},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5494066476821899},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.4398563802242279},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4358100891113281}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9270731210708618},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6995292901992798},{"id":"https://openalex.org/C29825287","wikidata":"https://www.wikidata.org/wiki/Q1427940","display_name":"Warning system","level":2,"score":0.6352747678756714},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.572952389717102},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5494066476821899},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4398563802242279},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4358100891113281},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2623330.2623694","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2623330.2623694","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.589.7256","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.589.7256","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.dabi.temple.edu/~zoran/papers/mohamed_kdd14.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.800000011920929,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G7743334336","display_name":null,"funder_award_id":"DARPAN66001-11-1-4183","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"}],"funders":[{"id":"https://openalex.org/F4320329942","display_name":"\u00c9cole doctorale des Sciences Chimiques","ror":null},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1487019984","https://openalex.org/W1699074795","https://openalex.org/W1963559133","https://openalex.org/W1973590682","https://openalex.org/W2029438113","https://openalex.org/W2041486528","https://openalex.org/W2062725912","https://openalex.org/W2063389953","https://openalex.org/W2078765398","https://openalex.org/W2098899567","https://openalex.org/W2125353221","https://openalex.org/W2138358456","https://openalex.org/W2162262128","https://openalex.org/W2207450561","https://openalex.org/W2402972623","https://openalex.org/W2403962807"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W4390569940","https://openalex.org/W2888392564","https://openalex.org/W4361193272","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W1986582023","https://openalex.org/W2883749686","https://openalex.org/W2966829450","https://openalex.org/W4315864862"],"abstract_inverted_index":{"Early":[0],"classification":[1,52,86,120],"of":[2,19,25,45,50,53,66,68,93,96,132],"time":[3,54,83,146],"series":[4,55,84,147],"is":[5,99,127,173,180],"prevalent":[6],"in":[7,27,81,134,166],"many":[8],"time-sensitive":[9],"applications":[10],"such":[11,88],"as,":[12],"but":[13],"not":[14,78],"limited":[15],"to,":[16],"early":[17,23,33,43,51,85,119,138,167],"warning":[18,24],"disease":[20],"outcome":[21],"and":[22,63,108,184],"crisis":[26],"stock":[28],"market.":[29],"\\textcolor{black}{":[30],"For":[31],"example,}":[32],"diagnosis":[34],"allows":[35],"physicians":[36],"to":[37,59,112,129,136,182],"design":[38],"appropriate":[39],"therapeutic":[40],"strategies":[41],"at":[42],"stages":[44],"diseases.":[46],"However,":[47],"practical":[48],"adaptation":[49],"requires":[56],"an":[57,117],"easy":[58],"understand":[60],"explanation":[61],"(interpretability)":[62],"a":[64,90,106],"measure":[65],"confidence":[67],"the":[69,152,159,170,177],"prediction":[70],"results":[71],"(uncertainty":[72],"estimates).":[73],"These":[74],"two":[75],"aspects":[76,98],"were":[77],"jointly":[79],"addressed":[80],"previous":[82],"studies,":[87],"that":[89,151,162],"difficult":[91],"choice":[92],"selecting":[94],"one":[95],"these":[97],"required.":[100],"In":[101,140],"this":[102],"study,":[103],"we":[104,124,149],"propose":[105],"simple":[107,181],"yet":[109],"effective":[110,175],"method":[111,154,161,172],"provide":[113,130],"uncertainty":[114,133],"estimates":[115,131,165],"for":[116],"interpretable":[118,137,186],"method.":[121],"The":[122],"question":[123],"address":[125],"here":[126],"\"how":[128],"regard":[135],"prediction.\"":[139],"our":[141],"extensive":[142],"evaluation":[143],"on":[144],"twenty":[145],"datasets":[148],"showed":[150],"proposed":[153,171],"has":[155],"several":[156],"advantages":[157],"over":[158],"state-of-the-art":[160,178],"provides":[163,185],"reliability":[164],"classification.":[168],"Namely,":[169],"more":[174],"than":[176],"method,":[179],"implement,":[183],"results.":[187]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":11},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":6},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}