{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:MF6RA6DVZG4UIKP23JUWA35JJA","short_pith_number":"pith:MF6RA6DV","schema_version":"1.0","canonical_sha256":"617d107875c9b94429fada69606fa948046db011295fdfed30ee6415d364a9fe","source":{"kind":"arxiv","id":"1806.03445","version":2},"attestation_state":"computed","paper":{"title":"Abstaining Classification When Error Costs are Unequal and Unknown","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"H. D. Cheng, Hongjiao Guan, Xianglong Tang, Yingtao Zhang","submitted_at":"2018-06-09T09:00:08Z","abstract_excerpt":"Abstaining classificaiton aims to reject to classify the easily misclassified examples, so it is an effective approach to increase the clasificaiton reliability and reduce the misclassification risk in the cost-sensitive applications. In such applications, different types of errors (false positive or false negative) usaully have unequal costs. And the error costs, which depend on specific applications, are usually unknown. However, current abstaining classification methods either do not distinguish the error types, or they need the cost information of misclassification and rejection, which are"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1806.03445","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-09T09:00:08Z","cross_cats_sorted":[],"title_canon_sha256":"81d141623203649f0c5de846cfd5d8e62095a1e302d4f73fba793a102084586d","abstract_canon_sha256":"ef5a2250d6662e64293e6fb4675b6056e6bf22aeb4930a79e3e00d56d2450892"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:52.207561Z","signature_b64":"VRIl9YFaQcgnX7lUjvP/r+P9bAR1YK+jwjWud+XaytEH0s85jpWTlsD/jLuRvzaREwT5F9JKTx/oniGrcd0jDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"617d107875c9b94429fada69606fa948046db011295fdfed30ee6415d364a9fe","last_reissued_at":"2026-05-18T00:09:52.206880Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:52.206880Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Abstaining Classification When Error Costs are Unequal and Unknown","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"H. D. Cheng, Hongjiao Guan, Xianglong Tang, Yingtao Zhang","submitted_at":"2018-06-09T09:00:08Z","abstract_excerpt":"Abstaining classificaiton aims to reject to classify the easily misclassified examples, so it is an effective approach to increase the clasificaiton reliability and reduce the misclassification risk in the cost-sensitive applications. In such applications, different types of errors (false positive or false negative) usaully have unequal costs. And the error costs, which depend on specific applications, are usually unknown. However, current abstaining classification methods either do not distinguish the error types, or they need the cost information of misclassification and rejection, which are"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.03445","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1806.03445","created_at":"2026-05-18T00:09:52.206992+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.03445v2","created_at":"2026-05-18T00:09:52.206992+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.03445","created_at":"2026-05-18T00:09:52.206992+00:00"},{"alias_kind":"pith_short_12","alias_value":"MF6RA6DVZG4U","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_16","alias_value":"MF6RA6DVZG4UIKP2","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_8","alias_value":"MF6RA6DV","created_at":"2026-05-18T12:32:37.024351+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.00856","citing_title":"Shapley in Context: Explaining Financial Language with Domain Expertise","ref_index":68,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MF6RA6DVZG4UIKP23JUWA35JJA","json":"https://pith.science/pith/MF6RA6DVZG4UIKP23JUWA35JJA.json","graph_json":"https://pith.science/api/pith-number/MF6RA6DVZG4UIKP23JUWA35JJA/graph.json","events_json":"https://pith.science/api/pith-number/MF6RA6DVZG4UIKP23JUWA35JJA/events.json","paper":"https://pith.science/paper/MF6RA6DV"},"agent_actions":{"view_html":"https://pith.science/pith/MF6RA6DVZG4UIKP23JUWA35JJA","download_json":"https://pith.science/pith/MF6RA6DVZG4UIKP23JUWA35JJA.json","view_paper":"https://pith.science/paper/MF6RA6DV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.03445&json=true","fetch_graph":"https://pith.science/api/pith-number/MF6RA6DVZG4UIKP23JUWA35JJA/graph.json","fetch_events":"https://pith.science/api/pith-number/MF6RA6DVZG4UIKP23JUWA35JJA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MF6RA6DVZG4UIKP23JUWA35JJA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MF6RA6DVZG4UIKP23JUWA35JJA/action/storage_attestation","attest_author":"https://pith.science/pith/MF6RA6DVZG4UIKP23JUWA35JJA/action/author_attestation","sign_citation":"https://pith.science/pith/MF6RA6DVZG4UIKP23JUWA35JJA/action/citation_signature","submit_replication":"https://pith.science/pith/MF6RA6DVZG4UIKP23JUWA35JJA/action/replication_record"}},"created_at":"2026-05-18T00:09:52.206992+00:00","updated_at":"2026-05-18T00:09:52.206992+00:00"}