{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ONXD3UR4LJUVIAFBLUE4YLPUYK","short_pith_number":"pith:ONXD3UR4","schema_version":"1.0","canonical_sha256":"736e3dd23c5a695400a15d09cc2df4c2a1c3e511a359087100491fb23c043abf","source":{"kind":"arxiv","id":"1804.06943","version":1},"attestation_state":"computed","paper":{"title":"K-Nearest Oracles Borderline Dynamic Classifier Ensemble Selection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Dayvid V. R. Oliveira, George D. C. Cavalcanti, Rafael M. O. Cruz, Robert Sabourin, Thyago N. Porpino","submitted_at":"2018-04-18T23:11:05Z","abstract_excerpt":"Dynamic Ensemble Selection (DES) techniques aim to select locally competent classifiers for the classification of each new test sample. Most DES techniques estimate the competence of classifiers using a given criterion over the region of competence of the test sample (its the nearest neighbors in the validation set). The K-Nearest Oracles Eliminate (KNORA-E) DES selects all classifiers that correctly classify all samples in the region of competence of the test sample, if such classifier exists, otherwise, it removes from the region of competence the sample that is furthest from the test sample"},"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":"1804.06943","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-18T23:11:05Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"8b447beead1d49118027907f08924351be1a98c5b3923a8a0a1c535e7c0f1af5","abstract_canon_sha256":"3d85a7b19404a68384346cbd3951d4db65649a4cb4088e12d4fa72bdbb5fa1a9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:53.340566Z","signature_b64":"/1Oq/qVREK8sJ0z2GKMgzhMZPWDcb4jtKWgNH2K11Rh47nmj4x1FWshwSd8+skhJxgxzqqJ/pOBe7jALlOygAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"736e3dd23c5a695400a15d09cc2df4c2a1c3e511a359087100491fb23c043abf","last_reissued_at":"2026-05-18T00:01:53.340001Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:53.340001Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"K-Nearest Oracles Borderline Dynamic Classifier Ensemble Selection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Dayvid V. R. Oliveira, George D. C. Cavalcanti, Rafael M. O. Cruz, Robert Sabourin, Thyago N. Porpino","submitted_at":"2018-04-18T23:11:05Z","abstract_excerpt":"Dynamic Ensemble Selection (DES) techniques aim to select locally competent classifiers for the classification of each new test sample. Most DES techniques estimate the competence of classifiers using a given criterion over the region of competence of the test sample (its the nearest neighbors in the validation set). The K-Nearest Oracles Eliminate (KNORA-E) DES selects all classifiers that correctly classify all samples in the region of competence of the test sample, if such classifier exists, otherwise, it removes from the region of competence the sample that is furthest from the test sample"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.06943","kind":"arxiv","version":1},"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":"1804.06943","created_at":"2026-05-18T00:01:53.340078+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.06943v1","created_at":"2026-05-18T00:01:53.340078+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.06943","created_at":"2026-05-18T00:01:53.340078+00:00"},{"alias_kind":"pith_short_12","alias_value":"ONXD3UR4LJUV","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_16","alias_value":"ONXD3UR4LJUVIAFB","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_8","alias_value":"ONXD3UR4","created_at":"2026-05-18T12:32:43.782077+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ONXD3UR4LJUVIAFBLUE4YLPUYK","json":"https://pith.science/pith/ONXD3UR4LJUVIAFBLUE4YLPUYK.json","graph_json":"https://pith.science/api/pith-number/ONXD3UR4LJUVIAFBLUE4YLPUYK/graph.json","events_json":"https://pith.science/api/pith-number/ONXD3UR4LJUVIAFBLUE4YLPUYK/events.json","paper":"https://pith.science/paper/ONXD3UR4"},"agent_actions":{"view_html":"https://pith.science/pith/ONXD3UR4LJUVIAFBLUE4YLPUYK","download_json":"https://pith.science/pith/ONXD3UR4LJUVIAFBLUE4YLPUYK.json","view_paper":"https://pith.science/paper/ONXD3UR4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.06943&json=true","fetch_graph":"https://pith.science/api/pith-number/ONXD3UR4LJUVIAFBLUE4YLPUYK/graph.json","fetch_events":"https://pith.science/api/pith-number/ONXD3UR4LJUVIAFBLUE4YLPUYK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ONXD3UR4LJUVIAFBLUE4YLPUYK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ONXD3UR4LJUVIAFBLUE4YLPUYK/action/storage_attestation","attest_author":"https://pith.science/pith/ONXD3UR4LJUVIAFBLUE4YLPUYK/action/author_attestation","sign_citation":"https://pith.science/pith/ONXD3UR4LJUVIAFBLUE4YLPUYK/action/citation_signature","submit_replication":"https://pith.science/pith/ONXD3UR4LJUVIAFBLUE4YLPUYK/action/replication_record"}},"created_at":"2026-05-18T00:01:53.340078+00:00","updated_at":"2026-05-18T00:01:53.340078+00:00"}