{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:7WU7MDGM3ZVSVQ4G7EZBATKSGE","short_pith_number":"pith:7WU7MDGM","canonical_record":{"source":{"id":"2409.19751","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-29T16:02:32Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"36492a089a2c29d21325b68f6e40972ea19426573dda8c93b9d856026086e804","abstract_canon_sha256":"2748c5da6e43c5e17bf0dbb485e97b8726fc5a7faa7c54a692e7d4cd7e59b567"},"schema_version":"1.0"},"canonical_sha256":"fda9f60cccde6b2ac386f932104d52311f67f2c9baaa60f785ac526ec961010e","source":{"kind":"arxiv","id":"2409.19751","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.19751","created_at":"2026-07-05T09:13:21Z"},{"alias_kind":"arxiv_version","alias_value":"2409.19751v1","created_at":"2026-07-05T09:13:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.19751","created_at":"2026-07-05T09:13:21Z"},{"alias_kind":"pith_short_12","alias_value":"7WU7MDGM3ZVS","created_at":"2026-07-05T09:13:21Z"},{"alias_kind":"pith_short_16","alias_value":"7WU7MDGM3ZVSVQ4G","created_at":"2026-07-05T09:13:21Z"},{"alias_kind":"pith_short_8","alias_value":"7WU7MDGM","created_at":"2026-07-05T09:13:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:7WU7MDGM3ZVSVQ4G7EZBATKSGE","target":"record","payload":{"canonical_record":{"source":{"id":"2409.19751","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-29T16:02:32Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"36492a089a2c29d21325b68f6e40972ea19426573dda8c93b9d856026086e804","abstract_canon_sha256":"2748c5da6e43c5e17bf0dbb485e97b8726fc5a7faa7c54a692e7d4cd7e59b567"},"schema_version":"1.0"},"canonical_sha256":"fda9f60cccde6b2ac386f932104d52311f67f2c9baaa60f785ac526ec961010e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:13:21.980789Z","signature_b64":"AJF1kKkFVJEqs4xMBGCYBDiHs1QScdfFLB8GPTUhptQAtaR4UCS24wdk/L1bFLt8hTlxZz/U6o/8LyqFp9RODA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fda9f60cccde6b2ac386f932104d52311f67f2c9baaa60f785ac526ec961010e","last_reissued_at":"2026-07-05T09:13:21.980333Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:13:21.980333Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2409.19751","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":"2026-07-05T09:13:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XV4+smFBllLlCMDugIl7RxI3Y0wizFrCQk1dXAk6vdlRD4aZFmbmAlEMoYJQHuWdLdfcMOXW3RiAvd7I9MkRAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T14:26:36.869221Z"},"content_sha256":"c87715b8873f0adef6db9cc9396762c7eb82349ad210ed2dd341d502e915f630","schema_version":"1.0","event_id":"sha256:c87715b8873f0adef6db9cc9396762c7eb82349ad210ed2dd341d502e915f630"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:7WU7MDGM3ZVSVQ4G7EZBATKSGE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Balancing the Scales: A Comprehensive Study on Tackling Class Imbalance in Binary Classification","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Abhyuday Desai, Mohamed Abdelhamid","submitted_at":"2024-09-29T16:02:32Z","abstract_excerpt":"Class imbalance in binary classification tasks remains a significant challenge in machine learning, often resulting in poor performance on minority classes. This study comprehensively evaluates three widely-used strategies for handling class imbalance: Synthetic Minority Over-sampling Technique (SMOTE), Class Weights tuning, and Decision Threshold Calibration. We compare these methods against a baseline scenario of no-intervention across 15 diverse machine learning models and 30 datasets from various domains, conducting a total of 9,000 experiments. Performance was primarily assessed using the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.19751","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/2409.19751/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":"2026-07-05T09:13:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XSaPaCKWbtHcKJymklFHXbcN7VJXUxSQslb4MaLWrqGnDuTBclfPoryUPqY6O0sk2uRupKlsZbcLbT1clHwDDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T14:26:36.869766Z"},"content_sha256":"14bc36695922e0e8ef1a2861d21689240f00249f0458a490dd0e3fffb9adac7e","schema_version":"1.0","event_id":"sha256:14bc36695922e0e8ef1a2861d21689240f00249f0458a490dd0e3fffb9adac7e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7WU7MDGM3ZVSVQ4G7EZBATKSGE/bundle.json","state_url":"https://pith.science/pith/7WU7MDGM3ZVSVQ4G7EZBATKSGE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7WU7MDGM3ZVSVQ4G7EZBATKSGE/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":"2026-08-10T14:26:36Z","links":{"resolver":"https://pith.science/pith/7WU7MDGM3ZVSVQ4G7EZBATKSGE","bundle":"https://pith.science/pith/7WU7MDGM3ZVSVQ4G7EZBATKSGE/bundle.json","state":"https://pith.science/pith/7WU7MDGM3ZVSVQ4G7EZBATKSGE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7WU7MDGM3ZVSVQ4G7EZBATKSGE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:7WU7MDGM3ZVSVQ4G7EZBATKSGE","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":"2748c5da6e43c5e17bf0dbb485e97b8726fc5a7faa7c54a692e7d4cd7e59b567","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-29T16:02:32Z","title_canon_sha256":"36492a089a2c29d21325b68f6e40972ea19426573dda8c93b9d856026086e804"},"schema_version":"1.0","source":{"id":"2409.19751","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.19751","created_at":"2026-07-05T09:13:21Z"},{"alias_kind":"arxiv_version","alias_value":"2409.19751v1","created_at":"2026-07-05T09:13:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.19751","created_at":"2026-07-05T09:13:21Z"},{"alias_kind":"pith_short_12","alias_value":"7WU7MDGM3ZVS","created_at":"2026-07-05T09:13:21Z"},{"alias_kind":"pith_short_16","alias_value":"7WU7MDGM3ZVSVQ4G","created_at":"2026-07-05T09:13:21Z"},{"alias_kind":"pith_short_8","alias_value":"7WU7MDGM","created_at":"2026-07-05T09:13:21Z"}],"graph_snapshots":[{"event_id":"sha256:14bc36695922e0e8ef1a2861d21689240f00249f0458a490dd0e3fffb9adac7e","target":"graph","created_at":"2026-07-05T09:13:21Z","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/2409.19751/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Class imbalance in binary classification tasks remains a significant challenge in machine learning, often resulting in poor performance on minority classes. This study comprehensively evaluates three widely-used strategies for handling class imbalance: Synthetic Minority Over-sampling Technique (SMOTE), Class Weights tuning, and Decision Threshold Calibration. We compare these methods against a baseline scenario of no-intervention across 15 diverse machine learning models and 30 datasets from various domains, conducting a total of 9,000 experiments. Performance was primarily assessed using the","authors_text":"Abhyuday Desai, Mohamed Abdelhamid","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-29T16:02:32Z","title":"Balancing the Scales: A Comprehensive Study on Tackling Class Imbalance in Binary Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.19751","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:c87715b8873f0adef6db9cc9396762c7eb82349ad210ed2dd341d502e915f630","target":"record","created_at":"2026-07-05T09:13:21Z","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":"2748c5da6e43c5e17bf0dbb485e97b8726fc5a7faa7c54a692e7d4cd7e59b567","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-29T16:02:32Z","title_canon_sha256":"36492a089a2c29d21325b68f6e40972ea19426573dda8c93b9d856026086e804"},"schema_version":"1.0","source":{"id":"2409.19751","kind":"arxiv","version":1}},"canonical_sha256":"fda9f60cccde6b2ac386f932104d52311f67f2c9baaa60f785ac526ec961010e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fda9f60cccde6b2ac386f932104d52311f67f2c9baaa60f785ac526ec961010e","first_computed_at":"2026-07-05T09:13:21.980333Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:13:21.980333Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AJF1kKkFVJEqs4xMBGCYBDiHs1QScdfFLB8GPTUhptQAtaR4UCS24wdk/L1bFLt8hTlxZz/U6o/8LyqFp9RODA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:13:21.980789Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.19751","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c87715b8873f0adef6db9cc9396762c7eb82349ad210ed2dd341d502e915f630","sha256:14bc36695922e0e8ef1a2861d21689240f00249f0458a490dd0e3fffb9adac7e"],"state_sha256":"a9216a31ebb0e9278368dbdb27f7406d334f08e593924b58e8ff7f76c2063e43"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yvB1LaM+A/3Ws9ctmtd/OGvgps/FB6ysCAri6+Z+974R7CHxqCmyx0Bl6dq8Pctxj66xlLYcENg5yG+c0JPmCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T14:26:36.875167Z","bundle_sha256":"705449320097ff2574e26e163b49bb257eb8526832c9f90ebf7c5739480d5c62"}}