{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:2QVTWPCOMKFK5KWX6KZ4WA6JDK","short_pith_number":"pith:2QVTWPCO","schema_version":"1.0","canonical_sha256":"d42b3b3c4e628aaeaad7f2b3cb03c91a9451525d7acd64e84fe810a99e605e6c","source":{"kind":"arxiv","id":"2110.07843","version":3},"attestation_state":"computed","paper":{"title":"FOLD-R++: A Scalable Toolset for Automated Inductive Learning of Default Theories from Mixed Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Gopal Gupta, Huaduo Wang","submitted_at":"2021-10-15T03:55:13Z","abstract_excerpt":"FOLD-R is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data. It generates an (explainable) answer set programming (ASP) rule set for classification tasks. We present an improved FOLD-R algorithm, called FOLD-R++, that significantly increases the efficiency and scalability of FOLD-R by orders of magnitude. FOLD-R++ improves upon FOLD-R without compromising or losing information in the input training data during the encoding or feature selection phase. The FOLD-R++ algorithm is competitive in performance with the widely-used XGBoost a"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2110.07843","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-15T03:55:13Z","cross_cats_sorted":[],"title_canon_sha256":"ddcf98c86937741b1d37db1d2b4fe0c3ed993fb12e6a1310f37d8846ea51f458","abstract_canon_sha256":"e85808c27456bc0984d10c5fd0e3e8a7c978f8adff7623e469e7020d3c07ef60"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:56:31.751246Z","signature_b64":"Pra2OVnWAcpEdhL7jrmWsDeLUa8voJ6gEtaM4X1cJknR3RcBJBU53zWYwLohD8FZKH9+7oFseartowPzTTxRDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d42b3b3c4e628aaeaad7f2b3cb03c91a9451525d7acd64e84fe810a99e605e6c","last_reissued_at":"2026-07-05T03:56:31.750819Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:56:31.750819Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FOLD-R++: A Scalable Toolset for Automated Inductive Learning of Default Theories from Mixed Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Gopal Gupta, Huaduo Wang","submitted_at":"2021-10-15T03:55:13Z","abstract_excerpt":"FOLD-R is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data. It generates an (explainable) answer set programming (ASP) rule set for classification tasks. We present an improved FOLD-R algorithm, called FOLD-R++, that significantly increases the efficiency and scalability of FOLD-R by orders of magnitude. FOLD-R++ improves upon FOLD-R without compromising or losing information in the input training data during the encoding or feature selection phase. The FOLD-R++ algorithm is competitive in performance with the widely-used XGBoost a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.07843","kind":"arxiv","version":3},"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/2110.07843/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2110.07843","created_at":"2026-07-05T03:56:31.750876+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.07843v3","created_at":"2026-07-05T03:56:31.750876+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.07843","created_at":"2026-07-05T03:56:31.750876+00:00"},{"alias_kind":"pith_short_12","alias_value":"2QVTWPCOMKFK","created_at":"2026-07-05T03:56:31.750876+00:00"},{"alias_kind":"pith_short_16","alias_value":"2QVTWPCOMKFK5KWX","created_at":"2026-07-05T03:56:31.750876+00:00"},{"alias_kind":"pith_short_8","alias_value":"2QVTWPCO","created_at":"2026-07-05T03:56:31.750876+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2QVTWPCOMKFK5KWX6KZ4WA6JDK","json":"https://pith.science/pith/2QVTWPCOMKFK5KWX6KZ4WA6JDK.json","graph_json":"https://pith.science/api/pith-number/2QVTWPCOMKFK5KWX6KZ4WA6JDK/graph.json","events_json":"https://pith.science/api/pith-number/2QVTWPCOMKFK5KWX6KZ4WA6JDK/events.json","paper":"https://pith.science/paper/2QVTWPCO"},"agent_actions":{"view_html":"https://pith.science/pith/2QVTWPCOMKFK5KWX6KZ4WA6JDK","download_json":"https://pith.science/pith/2QVTWPCOMKFK5KWX6KZ4WA6JDK.json","view_paper":"https://pith.science/paper/2QVTWPCO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.07843&json=true","fetch_graph":"https://pith.science/api/pith-number/2QVTWPCOMKFK5KWX6KZ4WA6JDK/graph.json","fetch_events":"https://pith.science/api/pith-number/2QVTWPCOMKFK5KWX6KZ4WA6JDK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2QVTWPCOMKFK5KWX6KZ4WA6JDK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2QVTWPCOMKFK5KWX6KZ4WA6JDK/action/storage_attestation","attest_author":"https://pith.science/pith/2QVTWPCOMKFK5KWX6KZ4WA6JDK/action/author_attestation","sign_citation":"https://pith.science/pith/2QVTWPCOMKFK5KWX6KZ4WA6JDK/action/citation_signature","submit_replication":"https://pith.science/pith/2QVTWPCOMKFK5KWX6KZ4WA6JDK/action/replication_record"}},"created_at":"2026-07-05T03:56:31.750876+00:00","updated_at":"2026-07-05T03:56:31.750876+00:00"}