{"id":"https://openalex.org/W4321593500","doi":"https://doi.org/10.48550/arxiv.2302.10578","title":"Don't guess what's true: choose what's optimal. A probability transducer for machine-learning classifiers","display_name":"Don't guess what's true: choose what's optimal. A probability transducer for machine-learning classifiers","publication_year":2023,"publication_date":"2023-02-21","ids":{"openalex":"https://openalex.org/W4321593500","doi":"https://doi.org/10.48550/arxiv.2302.10578"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2302.10578","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.10578","pdf_url":"https://arxiv.org/pdf/2302.10578","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2302.10578","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080170796","display_name":"K. Dyrland","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dyrland, K.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019454276","display_name":"Alexander Selvikv\u00e5g Lundervold","orcid":"https://orcid.org/0000-0001-8663-4247"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lundervold, A. S.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5018856585","display_name":"PierGianLuca Porta Mana","orcid":"https://orcid.org/0000-0002-6070-0784"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mana, P. G. L. Porta","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9896000027656555,"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/T10211","display_name":"Computational Drug Discovery Methods","score":0.9896000027656555,"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/T14351","display_name":"Statistical and Computational Modeling","score":0.939300000667572,"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/classifier","display_name":"Classifier (UML)","score":0.682769775390625},{"id":"https://openalex.org/keywords/transducer","display_name":"Transducer","score":0.6765412092208862},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6721253395080566},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.6079466342926025},{"id":"https://openalex.org/keywords/conditional-probability","display_name":"Conditional probability","score":0.49369677901268005},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.48033061623573303},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.476106733083725},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4307028651237488},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3295114040374756},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3067985773086548},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21469515562057495},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09267041087150574}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.682769775390625},{"id":"https://openalex.org/C56318395","wikidata":"https://www.wikidata.org/wiki/Q215928","display_name":"Transducer","level":2,"score":0.6765412092208862},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6721253395080566},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.6079466342926025},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.49369677901268005},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.48033061623573303},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.476106733083725},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4307028651237488},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3295114040374756},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3067985773086548},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21469515562057495},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09267041087150574},{"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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2302.10578","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.10578","pdf_url":"https://arxiv.org/pdf/2302.10578","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2302.10578","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2302.10578","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2302.10578","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.10578","pdf_url":"https://arxiv.org/pdf/2302.10578","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.6899999976158142}],"awards":[{"id":"https://openalex.org/G2230720350","display_name":null,"funder_award_id":"Sigma2","funder_id":"https://openalex.org/F4320323299","funder_display_name":"Norges Forskningsr\u00e5d"},{"id":"https://openalex.org/G3245653913","display_name":null,"funder_award_id":"294594","funder_id":"https://openalex.org/F4320323299","funder_display_name":"Norges Forskningsr\u00e5d"}],"funders":[{"id":"https://openalex.org/F4320323299","display_name":"Norges Forskningsr\u00e5d","ror":"https://ror.org/00epmv149"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4321593500.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2012283803","https://openalex.org/W4384820447","https://openalex.org/W2072454424","https://openalex.org/W2117438306","https://openalex.org/W2185942010","https://openalex.org/W2260725127","https://openalex.org/W2004297762","https://openalex.org/W1992056405","https://openalex.org/W1966826629","https://openalex.org/W767846903"],"abstract_inverted_index":{"In":[0,116],"fields":[1],"such":[2],"as":[3],"medicine":[4],"and":[5,100,165],"drug":[6],"discovery,":[7],"the":[8,23,40,51,56,72,82,86,114,121,124,130,134,189,198,201,205,209,223,227,235],"ultimate":[9],"goal":[10],"of":[11,26,31,42,71,113,123,140,146,182,188,197,208,225],"a":[12,18,29,138,154,159,195,230],"classification":[13],"is":[14,75,92,151,238],"not":[15,35,80],"to":[16,21,62,76,95,109,128,171,176],"guess":[17],"class,":[19],"but":[20,84],"choose":[22],"optimal":[24,131],"course":[25],"action":[27],"among":[28,133,137],"set":[30,41,139],"possible":[32],"ones,":[33],"usually":[34],"in":[36,64,153,229],"one-one":[37],"correspondence":[38],"with":[39,118,158],"classes.":[43,52],"This":[44,90,149],"decision-theoretic":[45],"problem":[46,157],"requires":[47],"sensible":[48],"probabilities":[49,78,122],"for":[50,179,219],"Probabilities":[53],"conditional":[54,79],"on":[55,81,85],"features":[57],"are":[58],"computationally":[59],"almost":[60],"impossible":[61],"find":[63,129],"many":[65],"important":[66],"cases.":[67],"The":[68,163,185],"main":[69],"idea":[70,150],"present":[73],"work":[74],"calculate":[77],"features,":[83],"trained":[87],"classifier's":[88],"output.":[89],"calculation":[91,187],"cheap,":[93],"needs":[94],"be":[96,107,217],"made":[97],"only":[98],"once,":[99],"provides":[101],"an":[102],"output-to-probability":[103],"\"transducer\"":[104],"that":[105],"can":[106,216],"applied":[108],"all":[110,180],"future":[111],"outputs":[112],"classifier.":[115],"conjunction":[117],"problem-dependent":[119,183],"utilities,":[120],"transducer":[125,164,190,202],"allow":[126],"us":[127],"choice":[132],"classes":[135],"or":[136],"more":[141],"general":[142],"decisions,":[143],"by":[144],"means":[145],"expected-utility":[147],"maximization.":[148],"demonstrated":[152],"simplified":[155],"drug-discovery":[156],"highly":[160],"imbalanced":[161],"dataset.":[162],"utility":[166,207],"maximization":[167],"together":[168],"always":[169],"lead":[170],"improved":[172],"results,":[173],"sometimes":[174],"close":[175],"theoretical":[177],"maximum,":[178],"sets":[181],"utilities.":[184],"one-time-only":[186],"also":[191],"provides,":[192],"automatically:":[193],"(i)":[194],"quantification":[196],"uncertainty":[199],"about":[200],"itself;":[203],"(ii)":[204],"expected":[206],"augmented":[210],"algorithm":[211,220,228],"(including":[212],"its":[213],"uncertainty),":[214],"which":[215],"used":[218],"selection;":[221],"(iii)":[222],"possibility":[224],"using":[226],"\"generative":[231],"mode\",":[232],"useful":[233],"if":[234],"training":[236],"dataset":[237],"biased.":[239]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2023-02-24T00:00:00"}
