{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:RFYMFM6UGFK4XMNOZUXMIRUANU","short_pith_number":"pith:RFYMFM6U","canonical_record":{"source":{"id":"2111.12140","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-23T20:20:24Z","cross_cats_sorted":["cs.DB","stat.ML"],"title_canon_sha256":"0361540cc3e79a838acfe9d8d9dab366a7ec83b41aa8f1bea58470216b78a9d7","abstract_canon_sha256":"1aa0a9d6ef0f2bafd58200b5a477667120e6f56d86f2e4c29b7431a2927635a6"},"schema_version":"1.0"},"canonical_sha256":"8970c2b3d43155cbb1aecd2ec446806d0069be96181666816fad6dfc018b461a","source":{"kind":"arxiv","id":"2111.12140","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.12140","created_at":"2026-07-05T03:34:48Z"},{"alias_kind":"arxiv_version","alias_value":"2111.12140v1","created_at":"2026-07-05T03:34:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.12140","created_at":"2026-07-05T03:34:48Z"},{"alias_kind":"pith_short_12","alias_value":"RFYMFM6UGFK4","created_at":"2026-07-05T03:34:48Z"},{"alias_kind":"pith_short_16","alias_value":"RFYMFM6UGFK4XMNO","created_at":"2026-07-05T03:34:48Z"},{"alias_kind":"pith_short_8","alias_value":"RFYMFM6U","created_at":"2026-07-05T03:34:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:RFYMFM6UGFK4XMNOZUXMIRUANU","target":"record","payload":{"canonical_record":{"source":{"id":"2111.12140","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-23T20:20:24Z","cross_cats_sorted":["cs.DB","stat.ML"],"title_canon_sha256":"0361540cc3e79a838acfe9d8d9dab366a7ec83b41aa8f1bea58470216b78a9d7","abstract_canon_sha256":"1aa0a9d6ef0f2bafd58200b5a477667120e6f56d86f2e4c29b7431a2927635a6"},"schema_version":"1.0"},"canonical_sha256":"8970c2b3d43155cbb1aecd2ec446806d0069be96181666816fad6dfc018b461a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:34:48.634733Z","signature_b64":"uPOr0tCbfiEBIDjGpj49aa9vZvz0hJV20Hopq4bcEUoBXbSSiyblYV+bJF3bt2QNA1M6JJi1uZ981v+20G0TAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8970c2b3d43155cbb1aecd2ec446806d0069be96181666816fad6dfc018b461a","last_reissued_at":"2026-07-05T03:34:48.634291Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:34:48.634291Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2111.12140","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:34:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8UePvvMqUgJxkMmIgOkBSFQ3nytjWYUF/HBdu9PVmuol7dp+COPeaOOrpOkFcDtRkFXkRc4ENObUEro6ST8tCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T16:42:10.073883Z"},"content_sha256":"e34692a97c0e6f4f79fed0e7a52a2bbabc843c3e8d893f56ba56dd486d6195dc","schema_version":"1.0","event_id":"sha256:e34692a97c0e6f4f79fed0e7a52a2bbabc843c3e8d893f56ba56dd486d6195dc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:RFYMFM6UGFK4XMNOZUXMIRUANU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Filter Methods for Feature Selection in Supervised Machine Learning Applications -- Review and Benchmark","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.DB","stat.ML"],"primary_cat":"cs.LG","authors_text":"Konstantin Hopf, Sascha Reifenrath","submitted_at":"2021-11-23T20:20:24Z","abstract_excerpt":"The amount of data for machine learning (ML) applications is constantly growing. Not only the number of observations, especially the number of measured variables (features) increases with ongoing digitization. Selecting the most appropriate features for predictive modeling is an important lever for the success of ML applications in business and research. Feature selection methods (FSM) that are independent of a certain ML algorithm - so-called filter methods - have been numerously suggested, but little guidance for researchers and quantitative modelers exists to choose appropriate approaches f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.12140","kind":"arxiv","version":1},"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/2111.12140/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":"2026-07-05T03:34:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yOteRwFiF3ANILIgtfNUMy/AEHsPWXETw/ZIM/z3N5U7mIY807FuRxD1KCLO0yvjGeLZeBgKUQtax3Kze1U5DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T16:42:10.074377Z"},"content_sha256":"25f9f5b0c631e7c2171b4b0fefe531ad67c142059569c78cd86590c46ac24ba8","schema_version":"1.0","event_id":"sha256:25f9f5b0c631e7c2171b4b0fefe531ad67c142059569c78cd86590c46ac24ba8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RFYMFM6UGFK4XMNOZUXMIRUANU/bundle.json","state_url":"https://pith.science/pith/RFYMFM6UGFK4XMNOZUXMIRUANU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RFYMFM6UGFK4XMNOZUXMIRUANU/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":"2026-08-20T16:42:10Z","links":{"resolver":"https://pith.science/pith/RFYMFM6UGFK4XMNOZUXMIRUANU","bundle":"https://pith.science/pith/RFYMFM6UGFK4XMNOZUXMIRUANU/bundle.json","state":"https://pith.science/pith/RFYMFM6UGFK4XMNOZUXMIRUANU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RFYMFM6UGFK4XMNOZUXMIRUANU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:RFYMFM6UGFK4XMNOZUXMIRUANU","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":"1aa0a9d6ef0f2bafd58200b5a477667120e6f56d86f2e4c29b7431a2927635a6","cross_cats_sorted":["cs.DB","stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-23T20:20:24Z","title_canon_sha256":"0361540cc3e79a838acfe9d8d9dab366a7ec83b41aa8f1bea58470216b78a9d7"},"schema_version":"1.0","source":{"id":"2111.12140","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.12140","created_at":"2026-07-05T03:34:48Z"},{"alias_kind":"arxiv_version","alias_value":"2111.12140v1","created_at":"2026-07-05T03:34:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.12140","created_at":"2026-07-05T03:34:48Z"},{"alias_kind":"pith_short_12","alias_value":"RFYMFM6UGFK4","created_at":"2026-07-05T03:34:48Z"},{"alias_kind":"pith_short_16","alias_value":"RFYMFM6UGFK4XMNO","created_at":"2026-07-05T03:34:48Z"},{"alias_kind":"pith_short_8","alias_value":"RFYMFM6U","created_at":"2026-07-05T03:34:48Z"}],"graph_snapshots":[{"event_id":"sha256:25f9f5b0c631e7c2171b4b0fefe531ad67c142059569c78cd86590c46ac24ba8","target":"graph","created_at":"2026-07-05T03:34:48Z","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/2111.12140/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The amount of data for machine learning (ML) applications is constantly growing. Not only the number of observations, especially the number of measured variables (features) increases with ongoing digitization. Selecting the most appropriate features for predictive modeling is an important lever for the success of ML applications in business and research. Feature selection methods (FSM) that are independent of a certain ML algorithm - so-called filter methods - have been numerously suggested, but little guidance for researchers and quantitative modelers exists to choose appropriate approaches f","authors_text":"Konstantin Hopf, Sascha Reifenrath","cross_cats":["cs.DB","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-23T20:20:24Z","title":"Filter Methods for Feature Selection in Supervised Machine Learning Applications -- Review and Benchmark"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.12140","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:e34692a97c0e6f4f79fed0e7a52a2bbabc843c3e8d893f56ba56dd486d6195dc","target":"record","created_at":"2026-07-05T03:34:48Z","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":"1aa0a9d6ef0f2bafd58200b5a477667120e6f56d86f2e4c29b7431a2927635a6","cross_cats_sorted":["cs.DB","stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-23T20:20:24Z","title_canon_sha256":"0361540cc3e79a838acfe9d8d9dab366a7ec83b41aa8f1bea58470216b78a9d7"},"schema_version":"1.0","source":{"id":"2111.12140","kind":"arxiv","version":1}},"canonical_sha256":"8970c2b3d43155cbb1aecd2ec446806d0069be96181666816fad6dfc018b461a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8970c2b3d43155cbb1aecd2ec446806d0069be96181666816fad6dfc018b461a","first_computed_at":"2026-07-05T03:34:48.634291Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:34:48.634291Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uPOr0tCbfiEBIDjGpj49aa9vZvz0hJV20Hopq4bcEUoBXbSSiyblYV+bJF3bt2QNA1M6JJi1uZ981v+20G0TAg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:34:48.634733Z","signed_message":"canonical_sha256_bytes"},"source_id":"2111.12140","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e34692a97c0e6f4f79fed0e7a52a2bbabc843c3e8d893f56ba56dd486d6195dc","sha256:25f9f5b0c631e7c2171b4b0fefe531ad67c142059569c78cd86590c46ac24ba8"],"state_sha256":"a8c21e823cb00a411d19bcfd4c2bb816e8f61e7148ec17d7703c6ae2e137e454"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Sp1helA40OmH54dejgdzlyZ39kXLsg3Rs4lDLQHAHfnktpc1xsuRL3+WR/5rdho2YCxVbvxdFV57Gya4Lv10AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T16:42:10.078381Z","bundle_sha256":"c6ab036fe90110879831eea547861277a510a93480c58238d01c2421f3674f55"}}