{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:ZESJWSVB5FOPNQOZL4PFT3FHVQ","short_pith_number":"pith:ZESJWSVB","canonical_record":{"source":{"id":"1802.05844","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-02-16T06:18:06Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"0c261cee271099d9a569108421fc678e1c034c06773aefc9da989a72f636dc79","abstract_canon_sha256":"51893f374f199d55ff945e1a4595cad607edbdd0285dff51cd339a037c1d0d21"},"schema_version":"1.0"},"canonical_sha256":"c9249b4aa1e95cf6c1d95f1e59eca7ac2a1a7ec1e30d7d0dc7474ec3474c21a3","source":{"kind":"arxiv","id":"1802.05844","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.05844","created_at":"2026-05-17T23:58:14Z"},{"alias_kind":"arxiv_version","alias_value":"1802.05844v4","created_at":"2026-05-17T23:58:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.05844","created_at":"2026-05-17T23:58:14Z"},{"alias_kind":"pith_short_12","alias_value":"ZESJWSVB5FOP","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"ZESJWSVB5FOPNQOZ","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"ZESJWSVB","created_at":"2026-05-18T12:33:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:ZESJWSVB5FOPNQOZL4PFT3FHVQ","target":"record","payload":{"canonical_record":{"source":{"id":"1802.05844","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-02-16T06:18:06Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"0c261cee271099d9a569108421fc678e1c034c06773aefc9da989a72f636dc79","abstract_canon_sha256":"51893f374f199d55ff945e1a4595cad607edbdd0285dff51cd339a037c1d0d21"},"schema_version":"1.0"},"canonical_sha256":"c9249b4aa1e95cf6c1d95f1e59eca7ac2a1a7ec1e30d7d0dc7474ec3474c21a3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:14.617010Z","signature_b64":"ILaj9ln7aC8bhvuSNsldKLZ006IfpcXCIjzSITMlMz+2XAXRCyGLOKq0HlCJAxqsDmTCWCOdSMCIqEUU8saqCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c9249b4aa1e95cf6c1d95f1e59eca7ac2a1a7ec1e30d7d0dc7474ec3474c21a3","last_reissued_at":"2026-05-17T23:58:14.616413Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:14.616413Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.05844","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:58:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3h9gBMRWud+I40JZzpD/PQjcVdlAjeCIjzjLLDYZY0KgKNePxgPacdaKqzIx6Y/ksEirz9Ujd5XpH4WiRnNcCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T21:59:22.898435Z"},"content_sha256":"bc8bdc7b52bda2490002e695756e3b1ecaf11f1ce712bc380554779256a04db6","schema_version":"1.0","event_id":"sha256:bc8bdc7b52bda2490002e695756e3b1ecaf11f1ce712bc380554779256a04db6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:ZESJWSVB5FOPNQOZL4PFT3FHVQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Unified View of Causal and Non-causal Feature Selection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.AI","authors_text":"Jiuyong Li, Kui Yu, Lin Liu","submitted_at":"2018-02-16T06:18:06Z","abstract_excerpt":"In this paper, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal featu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.05844","kind":"arxiv","version":4},"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":"2026-05-17T23:58:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CP35vLi0kaMftcqH0qhMnXK+oDvgTBcij4G8Dx+WvC9gXqX7eGNPA9qNza0NImQhF/Q+0WNpUnoeERY4ziwMDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T21:59:22.898775Z"},"content_sha256":"22a893d59f9759228c371509b8782713dbe887a31488df17f5003d9b0e8285f3","schema_version":"1.0","event_id":"sha256:22a893d59f9759228c371509b8782713dbe887a31488df17f5003d9b0e8285f3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZESJWSVB5FOPNQOZL4PFT3FHVQ/bundle.json","state_url":"https://pith.science/pith/ZESJWSVB5FOPNQOZL4PFT3FHVQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZESJWSVB5FOPNQOZL4PFT3FHVQ/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":"2026-06-29T21:59:22Z","links":{"resolver":"https://pith.science/pith/ZESJWSVB5FOPNQOZL4PFT3FHVQ","bundle":"https://pith.science/pith/ZESJWSVB5FOPNQOZL4PFT3FHVQ/bundle.json","state":"https://pith.science/pith/ZESJWSVB5FOPNQOZL4PFT3FHVQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZESJWSVB5FOPNQOZL4PFT3FHVQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:ZESJWSVB5FOPNQOZL4PFT3FHVQ","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":"51893f374f199d55ff945e1a4595cad607edbdd0285dff51cd339a037c1d0d21","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-02-16T06:18:06Z","title_canon_sha256":"0c261cee271099d9a569108421fc678e1c034c06773aefc9da989a72f636dc79"},"schema_version":"1.0","source":{"id":"1802.05844","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.05844","created_at":"2026-05-17T23:58:14Z"},{"alias_kind":"arxiv_version","alias_value":"1802.05844v4","created_at":"2026-05-17T23:58:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.05844","created_at":"2026-05-17T23:58:14Z"},{"alias_kind":"pith_short_12","alias_value":"ZESJWSVB5FOP","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"ZESJWSVB5FOPNQOZ","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"ZESJWSVB","created_at":"2026-05-18T12:33:07Z"}],"graph_snapshots":[{"event_id":"sha256:22a893d59f9759228c371509b8782713dbe887a31488df17f5003d9b0e8285f3","target":"graph","created_at":"2026-05-17T23:58:14Z","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"},"paper":{"abstract_excerpt":"In this paper, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal featu","authors_text":"Jiuyong Li, Kui Yu, Lin Liu","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-02-16T06:18:06Z","title":"A Unified View of Causal and Non-causal Feature Selection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.05844","kind":"arxiv","version":4},"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:bc8bdc7b52bda2490002e695756e3b1ecaf11f1ce712bc380554779256a04db6","target":"record","created_at":"2026-05-17T23:58:14Z","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":"51893f374f199d55ff945e1a4595cad607edbdd0285dff51cd339a037c1d0d21","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-02-16T06:18:06Z","title_canon_sha256":"0c261cee271099d9a569108421fc678e1c034c06773aefc9da989a72f636dc79"},"schema_version":"1.0","source":{"id":"1802.05844","kind":"arxiv","version":4}},"canonical_sha256":"c9249b4aa1e95cf6c1d95f1e59eca7ac2a1a7ec1e30d7d0dc7474ec3474c21a3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c9249b4aa1e95cf6c1d95f1e59eca7ac2a1a7ec1e30d7d0dc7474ec3474c21a3","first_computed_at":"2026-05-17T23:58:14.616413Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:58:14.616413Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ILaj9ln7aC8bhvuSNsldKLZ006IfpcXCIjzSITMlMz+2XAXRCyGLOKq0HlCJAxqsDmTCWCOdSMCIqEUU8saqCw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:58:14.617010Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.05844","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bc8bdc7b52bda2490002e695756e3b1ecaf11f1ce712bc380554779256a04db6","sha256:22a893d59f9759228c371509b8782713dbe887a31488df17f5003d9b0e8285f3"],"state_sha256":"dda71669f5ed65483e1d8eb909d44427349223c806cc551fd853e97a332f677c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/D9sq/dgKFh0/0HfET+uILsqxhi6gWFxOQRANXmgBrECg0PvHGtKzqIRhL894KVMH5h8Cw4wseTDJ/Zqs3Q+Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-29T21:59:22.900700Z","bundle_sha256":"0d7285dfcd0067a9065868ee7b156ddc6b8bd98a8ec812778759dc3207d83e07"}}