{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:H6ASMTTAXGDSLLWII2DH5OX24C","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":"69a113ba0a2f50a44002fe1af725366b3b2ef50a21336429765113ca88d7fd26","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-01T03:32:41Z","title_canon_sha256":"a246304f0b8c0aa9c1a310e7660c6f33b2e8276ebdbd07b66486aadcbf46b69d"},"schema_version":"1.0","source":{"id":"2508.00954","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.00954","created_at":"2026-07-05T11:47:30Z"},{"alias_kind":"arxiv_version","alias_value":"2508.00954v1","created_at":"2026-07-05T11:47:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.00954","created_at":"2026-07-05T11:47:30Z"},{"alias_kind":"pith_short_12","alias_value":"H6ASMTTAXGDS","created_at":"2026-07-05T11:47:30Z"},{"alias_kind":"pith_short_16","alias_value":"H6ASMTTAXGDSLLWI","created_at":"2026-07-05T11:47:30Z"},{"alias_kind":"pith_short_8","alias_value":"H6ASMTTA","created_at":"2026-07-05T11:47:30Z"}],"graph_snapshots":[{"event_id":"sha256:a9cd5e09abcae8a3a5d2da9ad1f0c860188b64097e5600250367956a83c8d68e","target":"graph","created_at":"2026-07-05T11:47:30Z","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/2508.00954/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In machine learning, the process of feature selection involves finding a reduced subset of features that captures most of the information required to train an accurate and efficient model. This work presents FeatureCuts, a novel feature selection algorithm that adaptively selects the optimal feature cutoff after performing filter ranking. Evaluated on 14 publicly available datasets and one industry dataset, FeatureCuts achieved, on average, 15 percentage points more feature reduction and up to 99.6% less computation time while maintaining model performance, compared to existing state-of-the-ar","authors_text":"Andy Hu, Anna Leontjeva, Arman Abrahamyan, Cooper Doyle, Dan Jermyn, Devika Prasad, Luiz Pizzato, Nicholas Foord","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-01T03:32:41Z","title":"FeatureCuts: Feature Selection for Large Data by Optimizing the Cutoff"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.00954","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":"sha256:7cc6544cd60dc55e41a42bea4fb25ddd2b87eab0c9e113d274d468c076e8aefa","target":"record","created_at":"2026-07-05T11:47:30Z","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":"69a113ba0a2f50a44002fe1af725366b3b2ef50a21336429765113ca88d7fd26","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-01T03:32:41Z","title_canon_sha256":"a246304f0b8c0aa9c1a310e7660c6f33b2e8276ebdbd07b66486aadcbf46b69d"},"schema_version":"1.0","source":{"id":"2508.00954","kind":"arxiv","version":1}},"canonical_sha256":"3f81264e60b98725aec846867ebafae09581da51e6c1a3dcbbb86d9f90f9fa2b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3f81264e60b98725aec846867ebafae09581da51e6c1a3dcbbb86d9f90f9fa2b","first_computed_at":"2026-07-05T11:47:30.787092Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:47:30.787092Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zf9lMeQ/MESGxkR3AOKkh5Ha9rBGocmuZBxxKQDXunLnuTc0RmXEfng4ivNM44VgD/SlAqaSWlWQyS+Wi9L4Dg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:47:30.787562Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.00954","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7cc6544cd60dc55e41a42bea4fb25ddd2b87eab0c9e113d274d468c076e8aefa","sha256:a9cd5e09abcae8a3a5d2da9ad1f0c860188b64097e5600250367956a83c8d68e"],"state_sha256":"f97abd2a17beb67c7cd148d522fba1316582948694904f854376c8f1893dc579"}