{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:25NUO2J4U74ZX522UPSTFQAAEH","short_pith_number":"pith:25NUO2J4","canonical_record":{"source":{"id":"2203.15318","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-29T08:01:03Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1e5e2f00d0ba54680064732bdad89b5e27ca0f9f550260f0f19f07871cd6000f","abstract_canon_sha256":"95f9c8fd3b447fd87387ebedbdc36a8570758a71090a0c907d2a4f53cf148ddc"},"schema_version":"1.0"},"canonical_sha256":"d75b47693ca7f99bf75aa3e532c00021cf1a19e65cb464e7ad2f5754a18059a1","source":{"kind":"arxiv","id":"2203.15318","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.15318","created_at":"2026-07-05T04:09:23Z"},{"alias_kind":"arxiv_version","alias_value":"2203.15318v1","created_at":"2026-07-05T04:09:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.15318","created_at":"2026-07-05T04:09:23Z"},{"alias_kind":"pith_short_12","alias_value":"25NUO2J4U74Z","created_at":"2026-07-05T04:09:23Z"},{"alias_kind":"pith_short_16","alias_value":"25NUO2J4U74ZX522","created_at":"2026-07-05T04:09:23Z"},{"alias_kind":"pith_short_8","alias_value":"25NUO2J4","created_at":"2026-07-05T04:09:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:25NUO2J4U74ZX522UPSTFQAAEH","target":"record","payload":{"canonical_record":{"source":{"id":"2203.15318","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-29T08:01:03Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1e5e2f00d0ba54680064732bdad89b5e27ca0f9f550260f0f19f07871cd6000f","abstract_canon_sha256":"95f9c8fd3b447fd87387ebedbdc36a8570758a71090a0c907d2a4f53cf148ddc"},"schema_version":"1.0"},"canonical_sha256":"d75b47693ca7f99bf75aa3e532c00021cf1a19e65cb464e7ad2f5754a18059a1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:09:23.921221Z","signature_b64":"Tj81tzWGfPG9ourNyh8Oe72ANDZ6W1mR/8stJYWPbYwQswQFgvmXd5YDtXr/CkTOXLw4JfMCi/rG4sWwsM69CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d75b47693ca7f99bf75aa3e532c00021cf1a19e65cb464e7ad2f5754a18059a1","last_reissued_at":"2026-07-05T04:09:23.920853Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:09:23.920853Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.15318","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:09:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FG8FCm1erA99VUujZpFg6gGhUJDFHm79NbdWcGzeRGpnZrKbHzlarh7mvlKndbI+4TogduiRt7B22PzVdMdSCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T01:42:42.710500Z"},"content_sha256":"1f486914262aeb1ec57fc3787a067d97a68c1139ef8dffb04d314ac61ed97b72","schema_version":"1.0","event_id":"sha256:1f486914262aeb1ec57fc3787a067d97a68c1139ef8dffb04d314ac61ed97b72"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:25NUO2J4U74ZX522UPSTFQAAEH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Evolving Multi-Label Fuzzy Classifier","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Edwin Lughofer","submitted_at":"2022-03-29T08:01:03Z","abstract_excerpt":"Multi-label classification has attracted much attention in the machine learning community to address the problem of assigning single samples to more than one class at the same time. We propose an evolving multi-label fuzzy classifier (EFC-ML) which is able to self-adapt and self-evolve its structure with new incoming multi-label samples in an incremental, single-pass manner. It is based on a multi-output Takagi-Sugeno type architecture, where for each class a separate consequent hyper-plane is defined. The learning procedure embeds a locally weighted incremental correlation-based algorithm com"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.15318","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2203.15318/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:09:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9AzF5QjnT8+V55DQ1EcBYZmpH+D8pqz4V6cFBKBhLnluJ8B6oEs5pcGlHaAaf1j5SIokYkh5NsggtXEo/dBeBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T01:42:42.710890Z"},"content_sha256":"c81be51474fa5b5f6e17ce105bacddb24ebf81d8744790f5d965e91d180c0cf2","schema_version":"1.0","event_id":"sha256:c81be51474fa5b5f6e17ce105bacddb24ebf81d8744790f5d965e91d180c0cf2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/25NUO2J4U74ZX522UPSTFQAAEH/bundle.json","state_url":"https://pith.science/pith/25NUO2J4U74ZX522UPSTFQAAEH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/25NUO2J4U74ZX522UPSTFQAAEH/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-09T01:42:42Z","links":{"resolver":"https://pith.science/pith/25NUO2J4U74ZX522UPSTFQAAEH","bundle":"https://pith.science/pith/25NUO2J4U74ZX522UPSTFQAAEH/bundle.json","state":"https://pith.science/pith/25NUO2J4U74ZX522UPSTFQAAEH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/25NUO2J4U74ZX522UPSTFQAAEH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:25NUO2J4U74ZX522UPSTFQAAEH","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"95f9c8fd3b447fd87387ebedbdc36a8570758a71090a0c907d2a4f53cf148ddc","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-29T08:01:03Z","title_canon_sha256":"1e5e2f00d0ba54680064732bdad89b5e27ca0f9f550260f0f19f07871cd6000f"},"schema_version":"1.0","source":{"id":"2203.15318","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.15318","created_at":"2026-07-05T04:09:23Z"},{"alias_kind":"arxiv_version","alias_value":"2203.15318v1","created_at":"2026-07-05T04:09:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.15318","created_at":"2026-07-05T04:09:23Z"},{"alias_kind":"pith_short_12","alias_value":"25NUO2J4U74Z","created_at":"2026-07-05T04:09:23Z"},{"alias_kind":"pith_short_16","alias_value":"25NUO2J4U74ZX522","created_at":"2026-07-05T04:09:23Z"},{"alias_kind":"pith_short_8","alias_value":"25NUO2J4","created_at":"2026-07-05T04:09:23Z"}],"graph_snapshots":[{"event_id":"sha256:c81be51474fa5b5f6e17ce105bacddb24ebf81d8744790f5d965e91d180c0cf2","target":"graph","created_at":"2026-07-05T04:09:23Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2203.15318/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-label classification has attracted much attention in the machine learning community to address the problem of assigning single samples to more than one class at the same time. We propose an evolving multi-label fuzzy classifier (EFC-ML) which is able to self-adapt and self-evolve its structure with new incoming multi-label samples in an incremental, single-pass manner. It is based on a multi-output Takagi-Sugeno type architecture, where for each class a separate consequent hyper-plane is defined. The learning procedure embeds a locally weighted incremental correlation-based algorithm com","authors_text":"Edwin Lughofer","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-29T08:01:03Z","title":"Evolving Multi-Label Fuzzy Classifier"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.15318","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1f486914262aeb1ec57fc3787a067d97a68c1139ef8dffb04d314ac61ed97b72","target":"record","created_at":"2026-07-05T04:09:23Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"95f9c8fd3b447fd87387ebedbdc36a8570758a71090a0c907d2a4f53cf148ddc","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-29T08:01:03Z","title_canon_sha256":"1e5e2f00d0ba54680064732bdad89b5e27ca0f9f550260f0f19f07871cd6000f"},"schema_version":"1.0","source":{"id":"2203.15318","kind":"arxiv","version":1}},"canonical_sha256":"d75b47693ca7f99bf75aa3e532c00021cf1a19e65cb464e7ad2f5754a18059a1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d75b47693ca7f99bf75aa3e532c00021cf1a19e65cb464e7ad2f5754a18059a1","first_computed_at":"2026-07-05T04:09:23.920853Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:09:23.920853Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Tj81tzWGfPG9ourNyh8Oe72ANDZ6W1mR/8stJYWPbYwQswQFgvmXd5YDtXr/CkTOXLw4JfMCi/rG4sWwsM69CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:09:23.921221Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.15318","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1f486914262aeb1ec57fc3787a067d97a68c1139ef8dffb04d314ac61ed97b72","sha256:c81be51474fa5b5f6e17ce105bacddb24ebf81d8744790f5d965e91d180c0cf2"],"state_sha256":"23aff034a434b302b6f475fffc85f8c28bf2ce35b21231631c70033c272ae223"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"azsdMBaPK3kxb2JmuaPVeT1SYsyTWJdAEdhbxHi0J85IMHLH0eLpfAynCivAIxjTCuBWzUGG2dsS/1pXV0cgDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T01:42:42.712959Z","bundle_sha256":"5ed8ea3163ed2af2a0d61482dcf9ef5ce905ec94200cedb61460fd9e20ee0158"}}