{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:C5LHA3KBVGEITA7S4LT7FJO2M2","short_pith_number":"pith:C5LHA3KB","schema_version":"1.0","canonical_sha256":"1756706d41a9888983f2e2e7f2a5da6692bb05a68ec54256341637e80131db06","source":{"kind":"arxiv","id":"2411.03713","version":2},"attestation_state":"computed","paper":{"title":"Generalized Trusted Multi-view Classification Framework with Hierarchical Opinion Aggregation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Badong Chen, Cai Xu, Chuanqing Tang, Huangyi Deng, Lei Xing, Long Shi","submitted_at":"2024-11-06T07:27:55Z","abstract_excerpt":"Recently, multi-view learning has witnessed a considerable interest on the research of trusted decision-making. Previous methods are mainly inspired from an important paper published by Han et al. in 2021, which formulates a Trusted Multi-view Classification (TMC) framework that aggregates evidence from different views based on Dempster's combination rule. All these methods only consider inter-view aggregation, yet lacking exploitation of intra-view information. In this paper, we propose a generalized trusted multi-view classification framework with hierarchical opinion aggregation. This hiera"},"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":"2411.03713","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-06T07:27:55Z","cross_cats_sorted":[],"title_canon_sha256":"b0ff8d58fb51a9549c47ad12b338b769a45fad2a47f4bb3f6ca1a66fa2ef1774","abstract_canon_sha256":"e5133489f7ef63824d463c3f04b31d26d07df0c1bcc0b0f8e35ccf1d23d596a1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:43:58.366132Z","signature_b64":"ZA/25f3KzB/0VipOfI8xQvjBmArq9jGfWW+zMSSDujoyQhSARZChrHPi9KZFBiLtIkxPmAns+n7BBdaSYhyTBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1756706d41a9888983f2e2e7f2a5da6692bb05a68ec54256341637e80131db06","last_reissued_at":"2026-07-05T11:43:58.365635Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:43:58.365635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generalized Trusted Multi-view Classification Framework with Hierarchical Opinion Aggregation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Badong Chen, Cai Xu, Chuanqing Tang, Huangyi Deng, Lei Xing, Long Shi","submitted_at":"2024-11-06T07:27:55Z","abstract_excerpt":"Recently, multi-view learning has witnessed a considerable interest on the research of trusted decision-making. Previous methods are mainly inspired from an important paper published by Han et al. in 2021, which formulates a Trusted Multi-view Classification (TMC) framework that aggregates evidence from different views based on Dempster's combination rule. All these methods only consider inter-view aggregation, yet lacking exploitation of intra-view information. In this paper, we propose a generalized trusted multi-view classification framework with hierarchical opinion aggregation. This hiera"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.03713","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2411.03713/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2411.03713","created_at":"2026-07-05T11:43:58.365697+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.03713v2","created_at":"2026-07-05T11:43:58.365697+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.03713","created_at":"2026-07-05T11:43:58.365697+00:00"},{"alias_kind":"pith_short_12","alias_value":"C5LHA3KBVGEI","created_at":"2026-07-05T11:43:58.365697+00:00"},{"alias_kind":"pith_short_16","alias_value":"C5LHA3KBVGEITA7S","created_at":"2026-07-05T11:43:58.365697+00:00"},{"alias_kind":"pith_short_8","alias_value":"C5LHA3KB","created_at":"2026-07-05T11:43:58.365697+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.02084","citing_title":"Towards Comprehensive Information-theoretic Multi-view Learning","ref_index":4,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/C5LHA3KBVGEITA7S4LT7FJO2M2","json":"https://pith.science/pith/C5LHA3KBVGEITA7S4LT7FJO2M2.json","graph_json":"https://pith.science/api/pith-number/C5LHA3KBVGEITA7S4LT7FJO2M2/graph.json","events_json":"https://pith.science/api/pith-number/C5LHA3KBVGEITA7S4LT7FJO2M2/events.json","paper":"https://pith.science/paper/C5LHA3KB"},"agent_actions":{"view_html":"https://pith.science/pith/C5LHA3KBVGEITA7S4LT7FJO2M2","download_json":"https://pith.science/pith/C5LHA3KBVGEITA7S4LT7FJO2M2.json","view_paper":"https://pith.science/paper/C5LHA3KB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.03713&json=true","fetch_graph":"https://pith.science/api/pith-number/C5LHA3KBVGEITA7S4LT7FJO2M2/graph.json","fetch_events":"https://pith.science/api/pith-number/C5LHA3KBVGEITA7S4LT7FJO2M2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C5LHA3KBVGEITA7S4LT7FJO2M2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C5LHA3KBVGEITA7S4LT7FJO2M2/action/storage_attestation","attest_author":"https://pith.science/pith/C5LHA3KBVGEITA7S4LT7FJO2M2/action/author_attestation","sign_citation":"https://pith.science/pith/C5LHA3KBVGEITA7S4LT7FJO2M2/action/citation_signature","submit_replication":"https://pith.science/pith/C5LHA3KBVGEITA7S4LT7FJO2M2/action/replication_record"}},"created_at":"2026-07-05T11:43:58.365697+00:00","updated_at":"2026-07-05T11:43:58.365697+00:00"}