{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:GOTVE35CRLPY2ANREW6ER6J4UR","short_pith_number":"pith:GOTVE35C","canonical_record":{"source":{"id":"1704.01770","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2017-04-06T10:06:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"36fb434f8c2e91b826da2050e018872a5b26e0852efa29253ac4379c42c5a826","abstract_canon_sha256":"2d7fcb4dca058c96dfc852562dd291c088acd74ab2e8e1e23dcf84f8bacda732"},"schema_version":"1.0"},"canonical_sha256":"33a7526fa28adf8d01b125bc48f93ca4740c03662567ed415158766d16a47cae","source":{"kind":"arxiv","id":"1704.01770","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.01770","created_at":"2026-05-18T00:39:17Z"},{"alias_kind":"arxiv_version","alias_value":"1704.01770v2","created_at":"2026-05-18T00:39:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.01770","created_at":"2026-05-18T00:39:17Z"},{"alias_kind":"pith_short_12","alias_value":"GOTVE35CRLPY","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_16","alias_value":"GOTVE35CRLPY2ANR","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_8","alias_value":"GOTVE35C","created_at":"2026-05-18T12:31:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:GOTVE35CRLPY2ANREW6ER6J4UR","target":"record","payload":{"canonical_record":{"source":{"id":"1704.01770","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2017-04-06T10:06:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"36fb434f8c2e91b826da2050e018872a5b26e0852efa29253ac4379c42c5a826","abstract_canon_sha256":"2d7fcb4dca058c96dfc852562dd291c088acd74ab2e8e1e23dcf84f8bacda732"},"schema_version":"1.0"},"canonical_sha256":"33a7526fa28adf8d01b125bc48f93ca4740c03662567ed415158766d16a47cae","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:17.900419Z","signature_b64":"wwie9oYqxSNcKc+jssCrwSKRVnWKJeCEq3Iuxft+buuan7ommiNQHjP2pUDthihOBsly7QdU0e1bsQS66mBAAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"33a7526fa28adf8d01b125bc48f93ca4740c03662567ed415158766d16a47cae","last_reissued_at":"2026-05-18T00:39:17.899744Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:17.899744Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1704.01770","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:39:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SpwVoKRRjX1sUg0S9hBj+eP8hKM6rBQ9YS6GFGHFW59d8jN2QdCjLRIOttgtdPtOEKlJRDYtX9LmiQbH+xTJBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T18:50:31.359557Z"},"content_sha256":"8f38bf44b5332667a94f3c36eb5b1429c06641393eb9af35e2d0fe789c9a45fa","schema_version":"1.0","event_id":"sha256:8f38bf44b5332667a94f3c36eb5b1429c06641393eb9af35e2d0fe789c9a45fa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:GOTVE35CRLPY2ANREW6ER6J4UR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enabling Smart Data: Noise filtering in Big Data classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DB","authors_text":"Diego Garc\\'ia-Gil, Francisco Herrera, Juli\\'an Luengo, Salvador Garc\\'ia","submitted_at":"2017-04-06T10:06:52Z","abstract_excerpt":"In any knowledge discovery process the value of extracted knowledge is directly related to the quality of the data used. Big Data problems, generated by massive growth in the scale of data observed in recent years, also follow the same dictate. A common problem affecting data quality is the presence of noise, particularly in classification problems, where label noise refers to the incorrect labeling of training instances, and is known to be a very disruptive feature of data. However, in this Big Data era, the massive growth in the scale of the data poses a challenge to traditional proposals cr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.01770","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:39:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UdWsz22d1ao1S5At7wXRC9EGBU+yw9Jtrp84ymVqqBeM7evtg0TBRqv7SvIPJ4Xn7hrpphUkx3bQ/NB+twFhBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T18:50:31.359957Z"},"content_sha256":"8b57c6069c69a7650d48666938ece5b205a124d83c69006285ae2b0d5f90b1d7","schema_version":"1.0","event_id":"sha256:8b57c6069c69a7650d48666938ece5b205a124d83c69006285ae2b0d5f90b1d7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GOTVE35CRLPY2ANREW6ER6J4UR/bundle.json","state_url":"https://pith.science/pith/GOTVE35CRLPY2ANREW6ER6J4UR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GOTVE35CRLPY2ANREW6ER6J4UR/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":"2026-05-30T18:50:31Z","links":{"resolver":"https://pith.science/pith/GOTVE35CRLPY2ANREW6ER6J4UR","bundle":"https://pith.science/pith/GOTVE35CRLPY2ANREW6ER6J4UR/bundle.json","state":"https://pith.science/pith/GOTVE35CRLPY2ANREW6ER6J4UR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GOTVE35CRLPY2ANREW6ER6J4UR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:GOTVE35CRLPY2ANREW6ER6J4UR","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":"2d7fcb4dca058c96dfc852562dd291c088acd74ab2e8e1e23dcf84f8bacda732","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2017-04-06T10:06:52Z","title_canon_sha256":"36fb434f8c2e91b826da2050e018872a5b26e0852efa29253ac4379c42c5a826"},"schema_version":"1.0","source":{"id":"1704.01770","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.01770","created_at":"2026-05-18T00:39:17Z"},{"alias_kind":"arxiv_version","alias_value":"1704.01770v2","created_at":"2026-05-18T00:39:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.01770","created_at":"2026-05-18T00:39:17Z"},{"alias_kind":"pith_short_12","alias_value":"GOTVE35CRLPY","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_16","alias_value":"GOTVE35CRLPY2ANR","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_8","alias_value":"GOTVE35C","created_at":"2026-05-18T12:31:18Z"}],"graph_snapshots":[{"event_id":"sha256:8b57c6069c69a7650d48666938ece5b205a124d83c69006285ae2b0d5f90b1d7","target":"graph","created_at":"2026-05-18T00:39:17Z","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"},"paper":{"abstract_excerpt":"In any knowledge discovery process the value of extracted knowledge is directly related to the quality of the data used. Big Data problems, generated by massive growth in the scale of data observed in recent years, also follow the same dictate. A common problem affecting data quality is the presence of noise, particularly in classification problems, where label noise refers to the incorrect labeling of training instances, and is known to be a very disruptive feature of data. However, in this Big Data era, the massive growth in the scale of the data poses a challenge to traditional proposals cr","authors_text":"Diego Garc\\'ia-Gil, Francisco Herrera, Juli\\'an Luengo, Salvador Garc\\'ia","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2017-04-06T10:06:52Z","title":"Enabling Smart Data: Noise filtering in Big Data classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.01770","kind":"arxiv","version":2},"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":"sha256:8f38bf44b5332667a94f3c36eb5b1429c06641393eb9af35e2d0fe789c9a45fa","target":"record","created_at":"2026-05-18T00:39:17Z","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":"2d7fcb4dca058c96dfc852562dd291c088acd74ab2e8e1e23dcf84f8bacda732","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2017-04-06T10:06:52Z","title_canon_sha256":"36fb434f8c2e91b826da2050e018872a5b26e0852efa29253ac4379c42c5a826"},"schema_version":"1.0","source":{"id":"1704.01770","kind":"arxiv","version":2}},"canonical_sha256":"33a7526fa28adf8d01b125bc48f93ca4740c03662567ed415158766d16a47cae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"33a7526fa28adf8d01b125bc48f93ca4740c03662567ed415158766d16a47cae","first_computed_at":"2026-05-18T00:39:17.899744Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:39:17.899744Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wwie9oYqxSNcKc+jssCrwSKRVnWKJeCEq3Iuxft+buuan7ommiNQHjP2pUDthihOBsly7QdU0e1bsQS66mBAAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:39:17.900419Z","signed_message":"canonical_sha256_bytes"},"source_id":"1704.01770","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8f38bf44b5332667a94f3c36eb5b1429c06641393eb9af35e2d0fe789c9a45fa","sha256:8b57c6069c69a7650d48666938ece5b205a124d83c69006285ae2b0d5f90b1d7"],"state_sha256":"52720102a529c8f72055d8cc81ad4859d0e50a347de0be0f2db55c7268092422"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8jcvkHjwoWZNOHneujvWPLTMkDEv6uWpg1DA2tTQOo5lb5VFSBhHU1gxNvrCcT+RnAkBFM7zACnaVfNzQhHNCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T18:50:31.362500Z","bundle_sha256":"8392795e438a7b0ac0df4eb5e1e83171b27395529c3686f0774ba10b61947f82"}}