{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:A5GG5XH53LZFN3ZVPXOL6IM6FI","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":"2c6bb15150925d64ff5f2d1b1ec9c9a79e01421c8ef1d617960a881b1cd34d3c","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-09T10:37:12Z","title_canon_sha256":"00ae545c09f02dd2ef17313ebeb523cde9dee3b023079e782dc957fbfa59002c"},"schema_version":"1.0","source":{"id":"1911.03658","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.03658","created_at":"2026-07-05T00:18:14Z"},{"alias_kind":"arxiv_version","alias_value":"1911.03658v1","created_at":"2026-07-05T00:18:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.03658","created_at":"2026-07-05T00:18:14Z"},{"alias_kind":"pith_short_12","alias_value":"A5GG5XH53LZF","created_at":"2026-07-05T00:18:14Z"},{"alias_kind":"pith_short_16","alias_value":"A5GG5XH53LZFN3ZV","created_at":"2026-07-05T00:18:14Z"},{"alias_kind":"pith_short_8","alias_value":"A5GG5XH5","created_at":"2026-07-05T00:18:14Z"}],"graph_snapshots":[{"event_id":"sha256:805a59c814aeedfdefbbe3d96c640a13c01909e0317e5062dc2177c27fab8bdb","target":"graph","created_at":"2026-07-05T00:18:14Z","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/1911.03658/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In real-world applications, we can encounter situations when a well-trained model has to be used to predict from a damaged dataset. The damage caused by missing or corrupted values can be either on the level of individual instances or on the level of entire features. Both situations have a negative impact on the usability of the model on such a dataset. This paper focuses on the scenario where entire features are missing which can be understood as a specific case of transfer learning. Our aim is to experimentally research the influence of various imputation methods on the performance of severa","authors_text":"Daniel Va\\v{s}ata, Magda Friedjungov\\'a, Marcel Ji\\v{r}ina","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-09T10:37:12Z","title":"Missing Features Reconstruction and Its Impact on Classification Accuracy"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.03658","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:b1b07473370c3704a44b590d5b0c4171ec26af09ef16ddf8ae382af0c8a8eb4e","target":"record","created_at":"2026-07-05T00:18:14Z","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":"2c6bb15150925d64ff5f2d1b1ec9c9a79e01421c8ef1d617960a881b1cd34d3c","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-09T10:37:12Z","title_canon_sha256":"00ae545c09f02dd2ef17313ebeb523cde9dee3b023079e782dc957fbfa59002c"},"schema_version":"1.0","source":{"id":"1911.03658","kind":"arxiv","version":1}},"canonical_sha256":"074c6edcfddaf256ef357ddcbf219e2a1901bea3452c83012d10796a9f18fc8a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"074c6edcfddaf256ef357ddcbf219e2a1901bea3452c83012d10796a9f18fc8a","first_computed_at":"2026-07-05T00:18:14.783875Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:18:14.783875Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3Jqx42x+o0UUELdHn4qEVIBKMitXvZvD5DnKlcmZ274s3cQMMCHgj7z9nthg6KJ6ROk4w1pH1GWiMplErRL6Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:18:14.784375Z","signed_message":"canonical_sha256_bytes"},"source_id":"1911.03658","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b1b07473370c3704a44b590d5b0c4171ec26af09ef16ddf8ae382af0c8a8eb4e","sha256:805a59c814aeedfdefbbe3d96c640a13c01909e0317e5062dc2177c27fab8bdb"],"state_sha256":"ba6c760260016330ccb5759f7f2f7e56c4a20c7fddafbadbd42e3dc50d13688c"}