{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:UKNWMWXEN4JISH5Y22PXZE7TJV","short_pith_number":"pith:UKNWMWXE","canonical_record":{"source":{"id":"2409.03164","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-05T01:48:11Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"9057a2b77f90d3b1acd2a3435c102a810d882bdbf519e20a7e571b1fda79a4b1","abstract_canon_sha256":"bd6325c34ae015ea076798f36b30c0f2fca4b108e5150222a23558efbf54a89b"},"schema_version":"1.0"},"canonical_sha256":"a29b665ae46f12891fb8d69f7c93f34d6f1754050f29aa5b2ffe0ca63c650e1e","source":{"kind":"arxiv","id":"2409.03164","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.03164","created_at":"2026-07-05T09:55:34Z"},{"alias_kind":"arxiv_version","alias_value":"2409.03164v2","created_at":"2026-07-05T09:55:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.03164","created_at":"2026-07-05T09:55:34Z"},{"alias_kind":"pith_short_12","alias_value":"UKNWMWXEN4JI","created_at":"2026-07-05T09:55:34Z"},{"alias_kind":"pith_short_16","alias_value":"UKNWMWXEN4JISH5Y","created_at":"2026-07-05T09:55:34Z"},{"alias_kind":"pith_short_8","alias_value":"UKNWMWXE","created_at":"2026-07-05T09:55:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:UKNWMWXEN4JISH5Y22PXZE7TJV","target":"record","payload":{"canonical_record":{"source":{"id":"2409.03164","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-05T01:48:11Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"9057a2b77f90d3b1acd2a3435c102a810d882bdbf519e20a7e571b1fda79a4b1","abstract_canon_sha256":"bd6325c34ae015ea076798f36b30c0f2fca4b108e5150222a23558efbf54a89b"},"schema_version":"1.0"},"canonical_sha256":"a29b665ae46f12891fb8d69f7c93f34d6f1754050f29aa5b2ffe0ca63c650e1e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:55:34.524207Z","signature_b64":"UO7IZwnBBXOwmdCpDhgsclCsr7dqQmwkW5q7kxmiE9swltNNqtIorzEy3B1lHPxFcp8jjbHUPW8CjnOrXDcKBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a29b665ae46f12891fb8d69f7c93f34d6f1754050f29aa5b2ffe0ca63c650e1e","last_reissued_at":"2026-07-05T09:55:34.523726Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:55:34.523726Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2409.03164","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:55:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ySXHBb0DzuMbhiMbeWkQ21P6DuZF/g6P5qxjqxE3+oLRDIxcJDS7zV+a5AjjlSaheXeIUgwbNT4YLgWtwT9YBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T01:55:45.318474Z"},"content_sha256":"634b10d73bead281640931a6d36fb90aed24b504ea627a3bb3dea01aa0b053d1","schema_version":"1.0","event_id":"sha256:634b10d73bead281640931a6d36fb90aed24b504ea627a3bb3dea01aa0b053d1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:UKNWMWXEN4JISH5Y22PXZE7TJV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"RuleExplorer: A Scalable Matrix Visualization for Understanding Tree Ensemble Classifiers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.LG","authors_text":"Changjian Chen, Fan Yang, Hui Zhang, Jing Wu, Jun Yuan, Shixia Liu, Weikai Yang, Yao Ming, Zhen Li","submitted_at":"2024-09-05T01:48:11Z","abstract_excerpt":"The high performance of tree ensemble classifiers benefits from a large set of rules, which, in turn, makes the models hard to understand. To improve interpretability, existing methods extract a subset of rules for approximation using model reduction techniques. However, by focusing on the reduced rule set, these methods often lose fidelity and ignore anomalous rules that, despite their infrequency, play crucial roles in real-world applications. This paper introduces a scalable visual analysis method to explain tree ensemble classifiers that contain tens of thousands of rules. The key idea is "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.03164","kind":"arxiv","version":2},"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/2409.03164/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":"2026-07-05T09:55:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Dw8EsMiceCNVFfh6minJSm6rTfUu09XPH1rCB+MsV1JHsF9ok2uu4j72kh9LxhW2laJMVvEzLNxyMZ63aUxzCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T01:55:45.319211Z"},"content_sha256":"faf7dcc75a38014559a2b9f353c83accb1b0cb5ac9bc2a228491db5b41cfaf91","schema_version":"1.0","event_id":"sha256:faf7dcc75a38014559a2b9f353c83accb1b0cb5ac9bc2a228491db5b41cfaf91"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UKNWMWXEN4JISH5Y22PXZE7TJV/bundle.json","state_url":"https://pith.science/pith/UKNWMWXEN4JISH5Y22PXZE7TJV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UKNWMWXEN4JISH5Y22PXZE7TJV/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":"2026-08-15T01:55:45Z","links":{"resolver":"https://pith.science/pith/UKNWMWXEN4JISH5Y22PXZE7TJV","bundle":"https://pith.science/pith/UKNWMWXEN4JISH5Y22PXZE7TJV/bundle.json","state":"https://pith.science/pith/UKNWMWXEN4JISH5Y22PXZE7TJV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UKNWMWXEN4JISH5Y22PXZE7TJV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:UKNWMWXEN4JISH5Y22PXZE7TJV","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":"bd6325c34ae015ea076798f36b30c0f2fca4b108e5150222a23558efbf54a89b","cross_cats_sorted":["cs.GR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-05T01:48:11Z","title_canon_sha256":"9057a2b77f90d3b1acd2a3435c102a810d882bdbf519e20a7e571b1fda79a4b1"},"schema_version":"1.0","source":{"id":"2409.03164","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.03164","created_at":"2026-07-05T09:55:34Z"},{"alias_kind":"arxiv_version","alias_value":"2409.03164v2","created_at":"2026-07-05T09:55:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.03164","created_at":"2026-07-05T09:55:34Z"},{"alias_kind":"pith_short_12","alias_value":"UKNWMWXEN4JI","created_at":"2026-07-05T09:55:34Z"},{"alias_kind":"pith_short_16","alias_value":"UKNWMWXEN4JISH5Y","created_at":"2026-07-05T09:55:34Z"},{"alias_kind":"pith_short_8","alias_value":"UKNWMWXE","created_at":"2026-07-05T09:55:34Z"}],"graph_snapshots":[{"event_id":"sha256:faf7dcc75a38014559a2b9f353c83accb1b0cb5ac9bc2a228491db5b41cfaf91","target":"graph","created_at":"2026-07-05T09:55:34Z","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/2409.03164/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The high performance of tree ensemble classifiers benefits from a large set of rules, which, in turn, makes the models hard to understand. To improve interpretability, existing methods extract a subset of rules for approximation using model reduction techniques. However, by focusing on the reduced rule set, these methods often lose fidelity and ignore anomalous rules that, despite their infrequency, play crucial roles in real-world applications. This paper introduces a scalable visual analysis method to explain tree ensemble classifiers that contain tens of thousands of rules. The key idea is ","authors_text":"Changjian Chen, Fan Yang, Hui Zhang, Jing Wu, Jun Yuan, Shixia Liu, Weikai Yang, Yao Ming, Zhen Li","cross_cats":["cs.GR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-05T01:48:11Z","title":"RuleExplorer: A Scalable Matrix Visualization for Understanding Tree Ensemble Classifiers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.03164","kind":"arxiv","version":2},"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":"sha256:634b10d73bead281640931a6d36fb90aed24b504ea627a3bb3dea01aa0b053d1","target":"record","created_at":"2026-07-05T09:55:34Z","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":"bd6325c34ae015ea076798f36b30c0f2fca4b108e5150222a23558efbf54a89b","cross_cats_sorted":["cs.GR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-05T01:48:11Z","title_canon_sha256":"9057a2b77f90d3b1acd2a3435c102a810d882bdbf519e20a7e571b1fda79a4b1"},"schema_version":"1.0","source":{"id":"2409.03164","kind":"arxiv","version":2}},"canonical_sha256":"a29b665ae46f12891fb8d69f7c93f34d6f1754050f29aa5b2ffe0ca63c650e1e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a29b665ae46f12891fb8d69f7c93f34d6f1754050f29aa5b2ffe0ca63c650e1e","first_computed_at":"2026-07-05T09:55:34.523726Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:55:34.523726Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UO7IZwnBBXOwmdCpDhgsclCsr7dqQmwkW5q7kxmiE9swltNNqtIorzEy3B1lHPxFcp8jjbHUPW8CjnOrXDcKBg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:55:34.524207Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.03164","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:634b10d73bead281640931a6d36fb90aed24b504ea627a3bb3dea01aa0b053d1","sha256:faf7dcc75a38014559a2b9f353c83accb1b0cb5ac9bc2a228491db5b41cfaf91"],"state_sha256":"6febbd9706954d56e37f5abaa0d8b434f83d2437e2ea46ccc94d71daa2415bfc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b3TU3AMV97C0ikbBv/B152dPZMffpwgW7EbvI6LceJk9cRRg6P6CRIuiZR4NFX0cfvjEOUlJFzCU/VJLK/ICBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T01:55:45.323868Z","bundle_sha256":"5965dd75933c6f68fc60edc86adb1276d40ad0ad9e67362ca1353baa793b01df"}}