{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:73C25ARHADYBZPJ4KCELJKVEPA","short_pith_number":"pith:73C25ARH","schema_version":"1.0","canonical_sha256":"fec5ae822700f01cbd3c5088b4aaa478111706317bb7b30f42ceb90ec449e68d","source":{"kind":"arxiv","id":"2211.10502","version":3},"attestation_state":"computed","paper":{"title":"A Mathematical Programming Approach to Optimal Classification Forests","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"math.OC","authors_text":"Alberto Jap\\'on, Justo Puerto, Peter Zhang, V\\'ictor Blanco","submitted_at":"2022-11-18T20:33:08Z","abstract_excerpt":"This paper introduces Weighted Optimal Classification Forests (WOCFs), a new family of classifiers that takes advantage of an optimal ensemble of decision trees to derive accurate and interpretable classifiers. We propose a novel mathematical optimization-based methodology which simultaneously constructs a given number of trees, each of them providing a predicted class for the observations in the feature space. The classification rule is derived by assigning to each observation its most frequently predicted class among the trees. We provide a mixed integer linear programming formulation (MIP) "},"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":"2211.10502","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"math.OC","submitted_at":"2022-11-18T20:33:08Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"38b64f4d736d5a4a31eb96c04516152976ba8e16f1dea1e9ed2b9bd4508eb724","abstract_canon_sha256":"44568a9d2d33476d5f54c1591109808b5b1918d77e1787bd4b54fac20132eecd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:41:26.473811Z","signature_b64":"CFAPfJvIaRTWAUC+f3R+zTlY2omqjd3PhFgM9KZrxH36sFnE8A/Gd7p858KBLMwmeDmMpkOn8JislIm2whj8CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fec5ae822700f01cbd3c5088b4aaa478111706317bb7b30f42ceb90ec449e68d","last_reissued_at":"2026-07-05T09:41:26.473367Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:41:26.473367Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Mathematical Programming Approach to Optimal Classification Forests","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"math.OC","authors_text":"Alberto Jap\\'on, Justo Puerto, Peter Zhang, V\\'ictor Blanco","submitted_at":"2022-11-18T20:33:08Z","abstract_excerpt":"This paper introduces Weighted Optimal Classification Forests (WOCFs), a new family of classifiers that takes advantage of an optimal ensemble of decision trees to derive accurate and interpretable classifiers. We propose a novel mathematical optimization-based methodology which simultaneously constructs a given number of trees, each of them providing a predicted class for the observations in the feature space. The classification rule is derived by assigning to each observation its most frequently predicted class among the trees. We provide a mixed integer linear programming formulation (MIP) "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.10502","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/2211.10502/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":"2211.10502","created_at":"2026-07-05T09:41:26.473414+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.10502v3","created_at":"2026-07-05T09:41:26.473414+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.10502","created_at":"2026-07-05T09:41:26.473414+00:00"},{"alias_kind":"pith_short_12","alias_value":"73C25ARHADYB","created_at":"2026-07-05T09:41:26.473414+00:00"},{"alias_kind":"pith_short_16","alias_value":"73C25ARHADYBZPJ4","created_at":"2026-07-05T09:41:26.473414+00:00"},{"alias_kind":"pith_short_8","alias_value":"73C25ARH","created_at":"2026-07-05T09:41:26.473414+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.03722","citing_title":"Optimal probabilistic feature shifts for reclassification in tree ensembles","ref_index":3,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/73C25ARHADYBZPJ4KCELJKVEPA","json":"https://pith.science/pith/73C25ARHADYBZPJ4KCELJKVEPA.json","graph_json":"https://pith.science/api/pith-number/73C25ARHADYBZPJ4KCELJKVEPA/graph.json","events_json":"https://pith.science/api/pith-number/73C25ARHADYBZPJ4KCELJKVEPA/events.json","paper":"https://pith.science/paper/73C25ARH"},"agent_actions":{"view_html":"https://pith.science/pith/73C25ARHADYBZPJ4KCELJKVEPA","download_json":"https://pith.science/pith/73C25ARHADYBZPJ4KCELJKVEPA.json","view_paper":"https://pith.science/paper/73C25ARH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.10502&json=true","fetch_graph":"https://pith.science/api/pith-number/73C25ARHADYBZPJ4KCELJKVEPA/graph.json","fetch_events":"https://pith.science/api/pith-number/73C25ARHADYBZPJ4KCELJKVEPA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/73C25ARHADYBZPJ4KCELJKVEPA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/73C25ARHADYBZPJ4KCELJKVEPA/action/storage_attestation","attest_author":"https://pith.science/pith/73C25ARHADYBZPJ4KCELJKVEPA/action/author_attestation","sign_citation":"https://pith.science/pith/73C25ARHADYBZPJ4KCELJKVEPA/action/citation_signature","submit_replication":"https://pith.science/pith/73C25ARHADYBZPJ4KCELJKVEPA/action/replication_record"}},"created_at":"2026-07-05T09:41:26.473414+00:00","updated_at":"2026-07-05T09:41:26.473414+00:00"}