{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:TYN2HDJ3Y5WCDSFMFTTOC6ODXC","short_pith_number":"pith:TYN2HDJ3","canonical_record":{"source":{"id":"1606.08698","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-06-28T13:49:30Z","cross_cats_sorted":["stat.AP","stat.ML"],"title_canon_sha256":"525c7eb0792921de1b5fa9eab21f7164a3bf8b126b839e45f8d483cedd026304","abstract_canon_sha256":"e630a27777ddb46987426468cf7d1751d8497359eab53cf30cde87bbe54d80f2"},"schema_version":"1.0"},"canonical_sha256":"9e1ba38d3bc76c21c8ac2ce6e179c3b8befd42b594b662d406275ea76cb94cb1","source":{"kind":"arxiv","id":"1606.08698","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.08698","created_at":"2026-05-18T00:42:07Z"},{"alias_kind":"arxiv_version","alias_value":"1606.08698v3","created_at":"2026-05-18T00:42:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.08698","created_at":"2026-05-18T00:42:07Z"},{"alias_kind":"pith_short_12","alias_value":"TYN2HDJ3Y5WC","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_16","alias_value":"TYN2HDJ3Y5WCDSFM","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_8","alias_value":"TYN2HDJ3","created_at":"2026-05-18T12:30:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:TYN2HDJ3Y5WCDSFMFTTOC6ODXC","target":"record","payload":{"canonical_record":{"source":{"id":"1606.08698","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-06-28T13:49:30Z","cross_cats_sorted":["stat.AP","stat.ML"],"title_canon_sha256":"525c7eb0792921de1b5fa9eab21f7164a3bf8b126b839e45f8d483cedd026304","abstract_canon_sha256":"e630a27777ddb46987426468cf7d1751d8497359eab53cf30cde87bbe54d80f2"},"schema_version":"1.0"},"canonical_sha256":"9e1ba38d3bc76c21c8ac2ce6e179c3b8befd42b594b662d406275ea76cb94cb1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:07.718465Z","signature_b64":"ZEcf1vnf8cJmOZXvzsGpAnhBPvSlaGLaOgIXN2aBrqq08pVn7w12+DCZUmTOO8BayFxznr9QAwToO4SHqWy5BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e1ba38d3bc76c21c8ac2ce6e179c3b8befd42b594b662d406275ea76cb94cb1","last_reissued_at":"2026-05-18T00:42:07.717914Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:07.717914Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1606.08698","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:42:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JFaHbtNGxtIUl2taaZHqcUPjKaiQGhKY00QQhc4Hwp6AS6z6re7MENP2lNJ4NTkrAaqrCY62HAqvKvVLpOOlBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T14:53:33.588573Z"},"content_sha256":"7f021994624d0eb8e7407f408198d33b824a9e743e0532f036040858b5c3d90d","schema_version":"1.0","event_id":"sha256:7f021994624d0eb8e7407f408198d33b824a9e743e0532f036040858b5c3d90d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:TYN2HDJ3Y5WCDSFMFTTOC6ODXC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Reviving Threshold-Moving: a Simple Plug-in Bagging Ensemble for Binary and Multiclass Imbalanced Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Drazen Prelec, Guillem Collell, Kaustubh Patil","submitted_at":"2016-06-28T13:49:30Z","abstract_excerpt":"Class imbalance presents a major hurdle in the application of data mining methods. A common practice to deal with it is to create ensembles of classifiers that learn from resampled balanced data. For example, bagged decision trees combined with random undersampling (RUS) or the synthetic minority oversampling technique (SMOTE). However, most of the resampling methods entail asymmetric changes to the examples of different classes, which in turn can introduce its own biases in the model. Furthermore, those methods require a performance measure to be specified a priori before learning. An alterna"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.08698","kind":"arxiv","version":3},"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":"2026-05-18T00:42:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hwsoKQ4Po/1QVzM99VVwb7hfPy0vHYVExU34dkqMl7kII2RbuVh6A9dScgv7qUghJN+YsI2uiPviHUViILvpDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T14:53:33.588910Z"},"content_sha256":"6ef593974c0b8295b2ae465b78e807d48578fba3b7a0cd8233185fc6c8d02c00","schema_version":"1.0","event_id":"sha256:6ef593974c0b8295b2ae465b78e807d48578fba3b7a0cd8233185fc6c8d02c00"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TYN2HDJ3Y5WCDSFMFTTOC6ODXC/bundle.json","state_url":"https://pith.science/pith/TYN2HDJ3Y5WCDSFMFTTOC6ODXC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TYN2HDJ3Y5WCDSFMFTTOC6ODXC/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":"2026-06-30T14:53:33Z","links":{"resolver":"https://pith.science/pith/TYN2HDJ3Y5WCDSFMFTTOC6ODXC","bundle":"https://pith.science/pith/TYN2HDJ3Y5WCDSFMFTTOC6ODXC/bundle.json","state":"https://pith.science/pith/TYN2HDJ3Y5WCDSFMFTTOC6ODXC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TYN2HDJ3Y5WCDSFMFTTOC6ODXC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:TYN2HDJ3Y5WCDSFMFTTOC6ODXC","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":"e630a27777ddb46987426468cf7d1751d8497359eab53cf30cde87bbe54d80f2","cross_cats_sorted":["stat.AP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-06-28T13:49:30Z","title_canon_sha256":"525c7eb0792921de1b5fa9eab21f7164a3bf8b126b839e45f8d483cedd026304"},"schema_version":"1.0","source":{"id":"1606.08698","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.08698","created_at":"2026-05-18T00:42:07Z"},{"alias_kind":"arxiv_version","alias_value":"1606.08698v3","created_at":"2026-05-18T00:42:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.08698","created_at":"2026-05-18T00:42:07Z"},{"alias_kind":"pith_short_12","alias_value":"TYN2HDJ3Y5WC","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_16","alias_value":"TYN2HDJ3Y5WCDSFM","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_8","alias_value":"TYN2HDJ3","created_at":"2026-05-18T12:30:46Z"}],"graph_snapshots":[{"event_id":"sha256:6ef593974c0b8295b2ae465b78e807d48578fba3b7a0cd8233185fc6c8d02c00","target":"graph","created_at":"2026-05-18T00:42:07Z","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":"Class imbalance presents a major hurdle in the application of data mining methods. A common practice to deal with it is to create ensembles of classifiers that learn from resampled balanced data. For example, bagged decision trees combined with random undersampling (RUS) or the synthetic minority oversampling technique (SMOTE). However, most of the resampling methods entail asymmetric changes to the examples of different classes, which in turn can introduce its own biases in the model. Furthermore, those methods require a performance measure to be specified a priori before learning. An alterna","authors_text":"Drazen Prelec, Guillem Collell, Kaustubh Patil","cross_cats":["stat.AP","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-06-28T13:49:30Z","title":"Reviving Threshold-Moving: a Simple Plug-in Bagging Ensemble for Binary and Multiclass Imbalanced Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.08698","kind":"arxiv","version":3},"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":"sha256:7f021994624d0eb8e7407f408198d33b824a9e743e0532f036040858b5c3d90d","target":"record","created_at":"2026-05-18T00:42:07Z","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":"e630a27777ddb46987426468cf7d1751d8497359eab53cf30cde87bbe54d80f2","cross_cats_sorted":["stat.AP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-06-28T13:49:30Z","title_canon_sha256":"525c7eb0792921de1b5fa9eab21f7164a3bf8b126b839e45f8d483cedd026304"},"schema_version":"1.0","source":{"id":"1606.08698","kind":"arxiv","version":3}},"canonical_sha256":"9e1ba38d3bc76c21c8ac2ce6e179c3b8befd42b594b662d406275ea76cb94cb1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9e1ba38d3bc76c21c8ac2ce6e179c3b8befd42b594b662d406275ea76cb94cb1","first_computed_at":"2026-05-18T00:42:07.717914Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:42:07.717914Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZEcf1vnf8cJmOZXvzsGpAnhBPvSlaGLaOgIXN2aBrqq08pVn7w12+DCZUmTOO8BayFxznr9QAwToO4SHqWy5BA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:42:07.718465Z","signed_message":"canonical_sha256_bytes"},"source_id":"1606.08698","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7f021994624d0eb8e7407f408198d33b824a9e743e0532f036040858b5c3d90d","sha256:6ef593974c0b8295b2ae465b78e807d48578fba3b7a0cd8233185fc6c8d02c00"],"state_sha256":"51d3d6018fa9bb15657ecf603c76f3bdbcfb9b914da25eb9ba4ce1885914cb50"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mnzUzDs38f0uJuYD4AFmXPlZXcciQ3CA2tQ0Jhh8lNxeij79O29nRzPEg+2lcCJiN3EawHD96Zn6M4sZoxcgDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-30T14:53:33.590733Z","bundle_sha256":"7f92544df1494048cf63075d9a6646b12e8508b5b56d1d98c18c6ea1e3f621b0"}}