{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:EWNMBJFPFY5PTJJ4FUVCTOPOHQ","short_pith_number":"pith:EWNMBJFP","canonical_record":{"source":{"id":"1703.08283","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-03-24T04:26:22Z","cross_cats_sorted":[],"title_canon_sha256":"4caf2daa3b48964ed2003a4813158863970d36238b6126aeb5bd71fe32e99571","abstract_canon_sha256":"9a2ecc1c842c3cfcb03073a8d2e78cb4a2e7db9dda7e675adda887c5efe59a89"},"schema_version":"1.0"},"canonical_sha256":"259ac0a4af2e3af9a53c2d2a29b9ee3c1a526a73beba57f8288803735383bb4c","source":{"kind":"arxiv","id":"1703.08283","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.08283","created_at":"2026-05-18T00:00:09Z"},{"alias_kind":"arxiv_version","alias_value":"1703.08283v2","created_at":"2026-05-18T00:00:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.08283","created_at":"2026-05-18T00:00:09Z"},{"alias_kind":"pith_short_12","alias_value":"EWNMBJFPFY5P","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"EWNMBJFPFY5PTJJ4","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"EWNMBJFP","created_at":"2026-05-18T12:31:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:EWNMBJFPFY5PTJJ4FUVCTOPOHQ","target":"record","payload":{"canonical_record":{"source":{"id":"1703.08283","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-03-24T04:26:22Z","cross_cats_sorted":[],"title_canon_sha256":"4caf2daa3b48964ed2003a4813158863970d36238b6126aeb5bd71fe32e99571","abstract_canon_sha256":"9a2ecc1c842c3cfcb03073a8d2e78cb4a2e7db9dda7e675adda887c5efe59a89"},"schema_version":"1.0"},"canonical_sha256":"259ac0a4af2e3af9a53c2d2a29b9ee3c1a526a73beba57f8288803735383bb4c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:09.333916Z","signature_b64":"97jRaVAm2omEQIM89/12RiFjbclcnY/2zm8y24nT/xR9rgDOoF0YpNqqwg5kfc0BPeWsMX8etrwdVnEQRn8mDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"259ac0a4af2e3af9a53c2d2a29b9ee3c1a526a73beba57f8288803735383bb4c","last_reissued_at":"2026-05-18T00:00:09.333352Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:09.333352Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1703.08283","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":"2026-05-18T00:00:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yT0ySqFUN9MTCHy3qmI0djT9MA4YUFLMaM3zHqxmG4a7Z+BvLDI4he7+NXN9NWdUjOwjp197RnhbGY+5a4HbDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T09:13:29.127246Z"},"content_sha256":"e5c89e3a9e21e621011d28efa91291720ba26697196bcd80b6c372f0b241e893","schema_version":"1.0","event_id":"sha256:e5c89e3a9e21e621011d28efa91291720ba26697196bcd80b6c372f0b241e893"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:EWNMBJFPFY5PTJJ4FUVCTOPOHQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Experimental Identification of Hard Data Sets for Classification and Feature Selection Methods with Insights on Method Selection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Cuiju Luan, Guozhu Dong","submitted_at":"2017-03-24T04:26:22Z","abstract_excerpt":"The paper reports an experimentally identified list of benchmark data sets that are hard for representative classification and feature selection methods. This was done after systematically evaluating a total of 48 combinations of methods, involving eight state-of-the-art classification algorithms and six commonly used feature selection methods, on 129 data sets from the UCI repository (some data sets with known high classification accuracy were excluded). In this paper, a data set for classification is called hard if none of the 48 combinations can achieve an AUC over 0.8 and none of them can "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.08283","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":""},"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":"2026-05-18T00:00:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R66YKFO1D0NB9IFOFyEbW+k8kQtA4ERLS4YCQoxsgZ+r38riPbKC0t0W0RBnqBqh67ewpfxyWH8JirsP//K9Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T09:13:29.127611Z"},"content_sha256":"3d13bc3007ecfac8f89293ace4ea65687324cae5a56affac6919bfce65ed5c21","schema_version":"1.0","event_id":"sha256:3d13bc3007ecfac8f89293ace4ea65687324cae5a56affac6919bfce65ed5c21"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EWNMBJFPFY5PTJJ4FUVCTOPOHQ/bundle.json","state_url":"https://pith.science/pith/EWNMBJFPFY5PTJJ4FUVCTOPOHQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EWNMBJFPFY5PTJJ4FUVCTOPOHQ/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":"2026-06-12T09:13:29Z","links":{"resolver":"https://pith.science/pith/EWNMBJFPFY5PTJJ4FUVCTOPOHQ","bundle":"https://pith.science/pith/EWNMBJFPFY5PTJJ4FUVCTOPOHQ/bundle.json","state":"https://pith.science/pith/EWNMBJFPFY5PTJJ4FUVCTOPOHQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EWNMBJFPFY5PTJJ4FUVCTOPOHQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:EWNMBJFPFY5PTJJ4FUVCTOPOHQ","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":"9a2ecc1c842c3cfcb03073a8d2e78cb4a2e7db9dda7e675adda887c5efe59a89","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-03-24T04:26:22Z","title_canon_sha256":"4caf2daa3b48964ed2003a4813158863970d36238b6126aeb5bd71fe32e99571"},"schema_version":"1.0","source":{"id":"1703.08283","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.08283","created_at":"2026-05-18T00:00:09Z"},{"alias_kind":"arxiv_version","alias_value":"1703.08283v2","created_at":"2026-05-18T00:00:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.08283","created_at":"2026-05-18T00:00:09Z"},{"alias_kind":"pith_short_12","alias_value":"EWNMBJFPFY5P","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"EWNMBJFPFY5PTJJ4","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"EWNMBJFP","created_at":"2026-05-18T12:31:12Z"}],"graph_snapshots":[{"event_id":"sha256:3d13bc3007ecfac8f89293ace4ea65687324cae5a56affac6919bfce65ed5c21","target":"graph","created_at":"2026-05-18T00:00:09Z","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":"The paper reports an experimentally identified list of benchmark data sets that are hard for representative classification and feature selection methods. This was done after systematically evaluating a total of 48 combinations of methods, involving eight state-of-the-art classification algorithms and six commonly used feature selection methods, on 129 data sets from the UCI repository (some data sets with known high classification accuracy were excluded). In this paper, a data set for classification is called hard if none of the 48 combinations can achieve an AUC over 0.8 and none of them can ","authors_text":"Cuiju Luan, Guozhu Dong","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-03-24T04:26:22Z","title":"Experimental Identification of Hard Data Sets for Classification and Feature Selection Methods with Insights on Method Selection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.08283","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:e5c89e3a9e21e621011d28efa91291720ba26697196bcd80b6c372f0b241e893","target":"record","created_at":"2026-05-18T00:00:09Z","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":"9a2ecc1c842c3cfcb03073a8d2e78cb4a2e7db9dda7e675adda887c5efe59a89","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-03-24T04:26:22Z","title_canon_sha256":"4caf2daa3b48964ed2003a4813158863970d36238b6126aeb5bd71fe32e99571"},"schema_version":"1.0","source":{"id":"1703.08283","kind":"arxiv","version":2}},"canonical_sha256":"259ac0a4af2e3af9a53c2d2a29b9ee3c1a526a73beba57f8288803735383bb4c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"259ac0a4af2e3af9a53c2d2a29b9ee3c1a526a73beba57f8288803735383bb4c","first_computed_at":"2026-05-18T00:00:09.333352Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:00:09.333352Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"97jRaVAm2omEQIM89/12RiFjbclcnY/2zm8y24nT/xR9rgDOoF0YpNqqwg5kfc0BPeWsMX8etrwdVnEQRn8mDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:00:09.333916Z","signed_message":"canonical_sha256_bytes"},"source_id":"1703.08283","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e5c89e3a9e21e621011d28efa91291720ba26697196bcd80b6c372f0b241e893","sha256:3d13bc3007ecfac8f89293ace4ea65687324cae5a56affac6919bfce65ed5c21"],"state_sha256":"25ded68611fa1ca7ee05472544b1bba501d56002d3e51d6ce9584c9f0f107503"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AuCKFaCZKF1YdTkw71/RmouzTEtzLBuDUpgoedkRIUM6odYBTpe/b/NPLq1c1KYbyMbHJEfymZMz82q4s9XrBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-12T09:13:29.129637Z","bundle_sha256":"b621e92cf52b12d3b83cd2476770b2583b49d1eeaba4c1044e13f4395f6056b9"}}