{"id":"https://openalex.org/W7165943853","doi":"https://doi.org/10.48550/arxiv.2606.24959","title":"Reliable Conformal Prediction for Ordinal Classification Using the Ranked Probability Score","display_name":"Reliable Conformal Prediction for Ordinal Classification Using the Ranked Probability Score","publication_year":2026,"publication_date":"2026-06-23","ids":{"openalex":"https://openalex.org/W7165943853","doi":"https://doi.org/10.48550/arxiv.2606.24959"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.24959","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24959","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.24959","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036419833","display_name":"Stefan Haas","orcid":"https://orcid.org/0000-0001-9916-0060"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haas, Stefan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139351687","display_name":"Luca Killmaier","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Killmaier, Luca","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077121568","display_name":"Alireza Javanmardi","orcid":"https://orcid.org/0000-0002-4901-5989"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Javanmardi, Alireza","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139304614","display_name":"Eyke H\u00fcllermeier","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"H\u00fcllermeier, Eyke","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":false,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.4839000105857849,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.4839000105857849,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.22920000553131104,"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/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.02969999983906746,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.8202999830245972},{"id":"https://openalex.org/keywords/ordinal-data","display_name":"Ordinal data","score":0.6812000274658203},{"id":"https://openalex.org/keywords/ordinal-regression","display_name":"Ordinal regression","score":0.6298999786376953},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.44830000400543213},{"id":"https://openalex.org/keywords/contingency-table","display_name":"Contingency table","score":0.41359999775886536},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.3880999982357025},{"id":"https://openalex.org/keywords/ordinal-scale","display_name":"Ordinal Scale","score":0.38609999418258667}],"concepts":[{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.8202999830245972},{"id":"https://openalex.org/C85461838","wikidata":"https://www.wikidata.org/wiki/Q7100785","display_name":"Ordinal data","level":2,"score":0.6812000274658203},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6672000288963318},{"id":"https://openalex.org/C110313322","wikidata":"https://www.wikidata.org/wiki/Q7100793","display_name":"Ordinal regression","level":2,"score":0.6298999786376953},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4733999967575073},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.44830000400543213},{"id":"https://openalex.org/C91998498","wikidata":"https://www.wikidata.org/wiki/Q1541178","display_name":"Contingency table","level":2,"score":0.41359999775886536},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.3880999982357025},{"id":"https://openalex.org/C2909711754","wikidata":"https://www.wikidata.org/wiki/Q7100785","display_name":"Ordinal Scale","level":2,"score":0.38609999418258667},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.38260000944137573},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35429999232292175},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.329800009727478},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3248000144958496},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.3021000027656555},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.266400009393692},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.2515999972820282},{"id":"https://openalex.org/C65660741","wikidata":"https://www.wikidata.org/wiki/Q3952743","display_name":"Score","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.24959","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24959","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"doi:10.48550/arxiv.2606.24959","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24959","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Ordinal":[0],"classification":[1,53],"(OC)":[2],"arises":[3],"in":[4,82],"high-stakes":[5],"domains":[6],"such":[7],"as":[8,89],"medicine":[9],"and":[10,109,114,128,137,146],"finance,":[11],"where":[12],"uncertainty":[13],"quantification":[14],"must":[15],"account":[16],"for":[17,51],"the":[18,41,56,147],"severity":[19],"of":[20,43,92,149],"ordinal":[21,52,73,84,126,150],"errors.":[22],"Conformal":[23],"prediction":[24,28,85,98,135,143],"(CP)":[25],"provides":[26],"distribution-free":[27],"sets":[29,99,136],"with":[30],"marginal":[31],"coverage":[32],"guarantees;":[33],"however,":[34],"its":[35],"practical":[36],"effectiveness":[37],"depends":[38],"critically":[39],"on":[40,55],"choice":[42],"nonconformity":[44],"function.":[45],"We":[46],"introduce":[47],"a":[48,61,90,139],"CP":[49,132,155],"method":[50,103],"based":[54],"ranked":[57],"probability":[58],"score":[59],"(RPS),":[60],"proper":[62],"scoring":[63],"rule":[64],"defined":[65],"over":[66],"cumulative":[67],"predictive":[68],"distributions.":[69],"Although":[70],"it":[71,77],"reflects":[72],"risk":[74],"quite":[75],"naturally,":[76],"has":[78],"largely":[79],"been":[80],"neglected":[81],"conformal":[83],"(COP).":[86],"When":[87],"used":[88],"measure":[91],"nonconformity,":[93],"RPS":[94],"yields":[95],"median-centered":[96],"contiguous":[97,134],"by":[100],"construction.":[101],"The":[102],"is":[104],"model-agnostic,":[105],"supports":[106],"both":[107],"assessed":[108],"grouped":[110],"ordered":[111],"categorical":[112],"outcomes,":[113],"permits":[115],"efficient":[116],"implementation":[117],"compared":[118],"to":[119,153],"greedy":[120],"interval":[121],"selection":[122],"procedures.":[123],"Across":[124],"multiple":[125],"image":[127],"tabular":[129],"datasets,":[130],"RPS-based":[131],"produces":[133],"strikes":[138],"favorable":[140],"balance":[141],"between":[142],"set":[144],"width":[145],"magnitude":[148],"miscoverage":[151],"relative":[152],"existing":[154],"methods.":[156]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-26T00:00:00"}