{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:U54R2OKPZ7ZUBQ6YSELU2PDKMF","short_pith_number":"pith:U54R2OKP","canonical_record":{"source":{"id":"2301.09631","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-23T18:59:01Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"97f8c6cce8bd5a6e0878e88001cba25522c3be7ec596068f952261becfcd3f45","abstract_canon_sha256":"399ab6c16761e02041e12e7b7f23cea2156ca87638be1585d846b5cff8a797d5"},"schema_version":"1.0"},"canonical_sha256":"a7791d394fcff340c3d891174d3c6a614cddaf1d3ffc95f5f51ad2e454df2c6f","source":{"kind":"arxiv","id":"2301.09631","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.09631","created_at":"2026-07-05T05:35:02Z"},{"alias_kind":"arxiv_version","alias_value":"2301.09631v1","created_at":"2026-07-05T05:35:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.09631","created_at":"2026-07-05T05:35:02Z"},{"alias_kind":"pith_short_12","alias_value":"U54R2OKPZ7ZU","created_at":"2026-07-05T05:35:02Z"},{"alias_kind":"pith_short_16","alias_value":"U54R2OKPZ7ZUBQ6Y","created_at":"2026-07-05T05:35:02Z"},{"alias_kind":"pith_short_8","alias_value":"U54R2OKP","created_at":"2026-07-05T05:35:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:U54R2OKPZ7ZUBQ6YSELU2PDKMF","target":"record","payload":{"canonical_record":{"source":{"id":"2301.09631","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-23T18:59:01Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"97f8c6cce8bd5a6e0878e88001cba25522c3be7ec596068f952261becfcd3f45","abstract_canon_sha256":"399ab6c16761e02041e12e7b7f23cea2156ca87638be1585d846b5cff8a797d5"},"schema_version":"1.0"},"canonical_sha256":"a7791d394fcff340c3d891174d3c6a614cddaf1d3ffc95f5f51ad2e454df2c6f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:35:02.636071Z","signature_b64":"2iYaJY2WklXzgoUevOx1s6zGo479SBFlNQLtMocmX+NkWLKN0sHFu6epHbwMwDGneE8PXn87/FP+AVudbqkABQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a7791d394fcff340c3d891174d3c6a614cddaf1d3ffc95f5f51ad2e454df2c6f","last_reissued_at":"2026-07-05T05:35:02.635581Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:35:02.635581Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2301.09631","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":"2026-07-05T05:35:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kET9Va6qEUm1UFQfEowZb2kUUOqbDIlvu9jabeoGHW/Lipe5OOciUDeWKQVoxEUieTulFsbJb4m12a0kmIUcCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T09:56:37.784019Z"},"content_sha256":"26ff27f07846279cc9493a17aacb25a21da79b2d7af4145054900845ae0481dc","schema_version":"1.0","event_id":"sha256:26ff27f07846279cc9493a17aacb25a21da79b2d7af4145054900845ae0481dc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:U54R2OKPZ7ZUBQ6YSELU2PDKMF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Feature construction using explanations of individual predictions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Bo\\v{s}tjan Vouk, Marko Robnik-\\v{S}ikonja, Matej Guid","submitted_at":"2023-01-23T18:59:01Z","abstract_excerpt":"Feature construction can contribute to comprehensibility and performance of machine learning models. Unfortunately, it usually requires exhaustive search in the attribute space or time-consuming human involvement to generate meaningful features. We propose a novel heuristic approach for reducing the search space based on aggregation of instance-based explanations of predictive models. The proposed Explainable Feature Construction (EFC) methodology identifies groups of co-occurring attributes exposed by popular explanation methods, such as IME and SHAP. We empirically show that reducing the sea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.09631","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/2301.09631/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":"2026-07-05T05:35:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vHuAWkq4O9R+aXUqWOg9ZZi5W4MwgkCsfrGHtvhcfq61tu3BXpUzFsezgcHzuOEbVK7AFMeNSM2BxgFWQbxBCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T09:56:37.784394Z"},"content_sha256":"ac2837230e68feb82bf74e02dfdac868d9b3dfe8eaecc74c9072b0ce16670bed","schema_version":"1.0","event_id":"sha256:ac2837230e68feb82bf74e02dfdac868d9b3dfe8eaecc74c9072b0ce16670bed"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U54R2OKPZ7ZUBQ6YSELU2PDKMF/bundle.json","state_url":"https://pith.science/pith/U54R2OKPZ7ZUBQ6YSELU2PDKMF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U54R2OKPZ7ZUBQ6YSELU2PDKMF/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":"2026-07-07T09:56:37Z","links":{"resolver":"https://pith.science/pith/U54R2OKPZ7ZUBQ6YSELU2PDKMF","bundle":"https://pith.science/pith/U54R2OKPZ7ZUBQ6YSELU2PDKMF/bundle.json","state":"https://pith.science/pith/U54R2OKPZ7ZUBQ6YSELU2PDKMF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U54R2OKPZ7ZUBQ6YSELU2PDKMF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:U54R2OKPZ7ZUBQ6YSELU2PDKMF","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":"399ab6c16761e02041e12e7b7f23cea2156ca87638be1585d846b5cff8a797d5","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-23T18:59:01Z","title_canon_sha256":"97f8c6cce8bd5a6e0878e88001cba25522c3be7ec596068f952261becfcd3f45"},"schema_version":"1.0","source":{"id":"2301.09631","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.09631","created_at":"2026-07-05T05:35:02Z"},{"alias_kind":"arxiv_version","alias_value":"2301.09631v1","created_at":"2026-07-05T05:35:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.09631","created_at":"2026-07-05T05:35:02Z"},{"alias_kind":"pith_short_12","alias_value":"U54R2OKPZ7ZU","created_at":"2026-07-05T05:35:02Z"},{"alias_kind":"pith_short_16","alias_value":"U54R2OKPZ7ZUBQ6Y","created_at":"2026-07-05T05:35:02Z"},{"alias_kind":"pith_short_8","alias_value":"U54R2OKP","created_at":"2026-07-05T05:35:02Z"}],"graph_snapshots":[{"event_id":"sha256:ac2837230e68feb82bf74e02dfdac868d9b3dfe8eaecc74c9072b0ce16670bed","target":"graph","created_at":"2026-07-05T05:35:02Z","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/2301.09631/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Feature construction can contribute to comprehensibility and performance of machine learning models. Unfortunately, it usually requires exhaustive search in the attribute space or time-consuming human involvement to generate meaningful features. We propose a novel heuristic approach for reducing the search space based on aggregation of instance-based explanations of predictive models. The proposed Explainable Feature Construction (EFC) methodology identifies groups of co-occurring attributes exposed by popular explanation methods, such as IME and SHAP. We empirically show that reducing the sea","authors_text":"Bo\\v{s}tjan Vouk, Marko Robnik-\\v{S}ikonja, Matej Guid","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-23T18:59:01Z","title":"Feature construction using explanations of individual predictions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.09631","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":"sha256:26ff27f07846279cc9493a17aacb25a21da79b2d7af4145054900845ae0481dc","target":"record","created_at":"2026-07-05T05:35:02Z","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":"399ab6c16761e02041e12e7b7f23cea2156ca87638be1585d846b5cff8a797d5","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-23T18:59:01Z","title_canon_sha256":"97f8c6cce8bd5a6e0878e88001cba25522c3be7ec596068f952261becfcd3f45"},"schema_version":"1.0","source":{"id":"2301.09631","kind":"arxiv","version":1}},"canonical_sha256":"a7791d394fcff340c3d891174d3c6a614cddaf1d3ffc95f5f51ad2e454df2c6f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a7791d394fcff340c3d891174d3c6a614cddaf1d3ffc95f5f51ad2e454df2c6f","first_computed_at":"2026-07-05T05:35:02.635581Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:35:02.635581Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2iYaJY2WklXzgoUevOx1s6zGo479SBFlNQLtMocmX+NkWLKN0sHFu6epHbwMwDGneE8PXn87/FP+AVudbqkABQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:35:02.636071Z","signed_message":"canonical_sha256_bytes"},"source_id":"2301.09631","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:26ff27f07846279cc9493a17aacb25a21da79b2d7af4145054900845ae0481dc","sha256:ac2837230e68feb82bf74e02dfdac868d9b3dfe8eaecc74c9072b0ce16670bed"],"state_sha256":"d3412e60cc3bd8c3336d066ea4d38994b37af8087105d538cd3ec66348c74fcd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uMcJKdwRng13CpnbFw44jKz8oUfq+xdFk6x1W/YR/NCyO2O6dTsGnUhe8q8/MIsXsS2NeXM8DCBQmc2uaOMgAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T09:56:37.786258Z","bundle_sha256":"832ce76c312aca4e5130a3487d76add77108353f67ea44663b6fe2edb9a9e236"}}