{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:H7SCVULN6SF3PLTKA5ZOVXWKPN","short_pith_number":"pith:H7SCVULN","schema_version":"1.0","canonical_sha256":"3fe42ad16df48bb7ae6a0772eadeca7b4b909691ce8be960aae6cc38f232385a","source":{"kind":"arxiv","id":"1905.07297","version":1},"attestation_state":"computed","paper":{"title":"MOBA: A multi-objective bounded-abstention model for two-class cost-sensitive problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Hongjiao Guan","submitted_at":"2019-05-17T14:40:17Z","abstract_excerpt":"Abstaining classifiers have been widely used in cost-sensitive applications to avoid ambiguous classification and reduce the cost of misclassification. Previous abstaining classification models rely on cost information, such as a cost matrix or cost ratio. However, it is difficult to obtain or estimate costs in practical applications. Furthermore, these abstention models are typically restricted to a single optimization metric, which may not be the expected indicator when evaluating classification performance. To overcome such problems, a multi-objective bounded-abstention (MOBA) model is prop"},"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":"1905.07297","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-17T14:40:17Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"8ef41bace015a16c8bdf34a1e1312b2fcf52cea715de46e04da5bbbb26290854","abstract_canon_sha256":"d1fe7dd918ad439bc881f47011ee83b4863bcf25acf6f4236f40d5b90186abfa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:55.935002Z","signature_b64":"ENyt2pQKrKkpqOKTUhIvHrBdca7NSCCa3J8XSL82SO697wKOWZ4AtHkx4EJyOJY0lWN52eSNhYw1MLLDP+d3Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3fe42ad16df48bb7ae6a0772eadeca7b4b909691ce8be960aae6cc38f232385a","last_reissued_at":"2026-05-17T23:45:55.934481Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:55.934481Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MOBA: A multi-objective bounded-abstention model for two-class cost-sensitive problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Hongjiao Guan","submitted_at":"2019-05-17T14:40:17Z","abstract_excerpt":"Abstaining classifiers have been widely used in cost-sensitive applications to avoid ambiguous classification and reduce the cost of misclassification. Previous abstaining classification models rely on cost information, such as a cost matrix or cost ratio. However, it is difficult to obtain or estimate costs in practical applications. Furthermore, these abstention models are typically restricted to a single optimization metric, which may not be the expected indicator when evaluating classification performance. To overcome such problems, a multi-objective bounded-abstention (MOBA) model is prop"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.07297","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":"1905.07297","created_at":"2026-05-17T23:45:55.934562+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.07297v1","created_at":"2026-05-17T23:45:55.934562+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.07297","created_at":"2026-05-17T23:45:55.934562+00:00"},{"alias_kind":"pith_short_12","alias_value":"H7SCVULN6SF3","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_16","alias_value":"H7SCVULN6SF3PLTK","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_8","alias_value":"H7SCVULN","created_at":"2026-05-18T12:33:18.533446+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/H7SCVULN6SF3PLTKA5ZOVXWKPN","json":"https://pith.science/pith/H7SCVULN6SF3PLTKA5ZOVXWKPN.json","graph_json":"https://pith.science/api/pith-number/H7SCVULN6SF3PLTKA5ZOVXWKPN/graph.json","events_json":"https://pith.science/api/pith-number/H7SCVULN6SF3PLTKA5ZOVXWKPN/events.json","paper":"https://pith.science/paper/H7SCVULN"},"agent_actions":{"view_html":"https://pith.science/pith/H7SCVULN6SF3PLTKA5ZOVXWKPN","download_json":"https://pith.science/pith/H7SCVULN6SF3PLTKA5ZOVXWKPN.json","view_paper":"https://pith.science/paper/H7SCVULN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.07297&json=true","fetch_graph":"https://pith.science/api/pith-number/H7SCVULN6SF3PLTKA5ZOVXWKPN/graph.json","fetch_events":"https://pith.science/api/pith-number/H7SCVULN6SF3PLTKA5ZOVXWKPN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H7SCVULN6SF3PLTKA5ZOVXWKPN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H7SCVULN6SF3PLTKA5ZOVXWKPN/action/storage_attestation","attest_author":"https://pith.science/pith/H7SCVULN6SF3PLTKA5ZOVXWKPN/action/author_attestation","sign_citation":"https://pith.science/pith/H7SCVULN6SF3PLTKA5ZOVXWKPN/action/citation_signature","submit_replication":"https://pith.science/pith/H7SCVULN6SF3PLTKA5ZOVXWKPN/action/replication_record"}},"created_at":"2026-05-17T23:45:55.934562+00:00","updated_at":"2026-05-17T23:45:55.934562+00:00"}