{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:IYBMOFFW2KD2AJKVPJH6VA4EVP","short_pith_number":"pith:IYBMOFFW","schema_version":"1.0","canonical_sha256":"4602c714b6d287a025557a4fea8384abd1cccc6a49c9c5bc3774e4ae329ab97c","source":{"kind":"arxiv","id":"1410.0640","version":3},"attestation_state":"computed","paper":{"title":"Term-Weighting Learning via Genetic Programming for Text Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Alicia Morales-Reyes, Eduardo F. Morales, Hugo Jair Escalante, Manuel Montes-y-G\\'omez, Mario Graff, Mauricio A. Garc\\'ia-Lim\\'on","submitted_at":"2014-10-02T18:38:11Z","abstract_excerpt":"This paper describes a novel approach to learning term-weighting schemes (TWSs) in the context of text classification. In text mining a TWS determines the way in which documents will be represented in a vector space model, before applying a classifier. Whereas acceptable performance has been obtained with standard TWSs (e.g., Boolean and term-frequency schemes), the definition of TWSs has been traditionally an art. Further, it is still a difficult task to determine what is the best TWS for a particular problem and it is not clear yet, whether better schemes, than those currently available, can"},"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":"1410.0640","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2014-10-02T18:38:11Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5302e608de0b6b27dc1043be8610e118c9a1738b924ff4cccb6d2aa07c4857c7","abstract_canon_sha256":"703929e3196072de407d02c2af1af31fab4e9046c2ca0bbcf380a6c6f4b84a51"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:41:01.628724Z","signature_b64":"xcVhPxZBOyBmWMiPrIxRFignYbrNKnTTuQ7GscwjnIl01/cN77O0vYVQZ8kkIH7RoOGmfEBcM+hcFQcZBqytDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4602c714b6d287a025557a4fea8384abd1cccc6a49c9c5bc3774e4ae329ab97c","last_reissued_at":"2026-05-18T02:41:01.628112Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:41:01.628112Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Term-Weighting Learning via Genetic Programming for Text Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Alicia Morales-Reyes, Eduardo F. Morales, Hugo Jair Escalante, Manuel Montes-y-G\\'omez, Mario Graff, Mauricio A. Garc\\'ia-Lim\\'on","submitted_at":"2014-10-02T18:38:11Z","abstract_excerpt":"This paper describes a novel approach to learning term-weighting schemes (TWSs) in the context of text classification. In text mining a TWS determines the way in which documents will be represented in a vector space model, before applying a classifier. Whereas acceptable performance has been obtained with standard TWSs (e.g., Boolean and term-frequency schemes), the definition of TWSs has been traditionally an art. Further, it is still a difficult task to determine what is the best TWS for a particular problem and it is not clear yet, whether better schemes, than those currently available, can"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1410.0640","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":""},"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":"1410.0640","created_at":"2026-05-18T02:41:01.628202+00:00"},{"alias_kind":"arxiv_version","alias_value":"1410.0640v3","created_at":"2026-05-18T02:41:01.628202+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1410.0640","created_at":"2026-05-18T02:41:01.628202+00:00"},{"alias_kind":"pith_short_12","alias_value":"IYBMOFFW2KD2","created_at":"2026-05-18T12:28:33.132498+00:00"},{"alias_kind":"pith_short_16","alias_value":"IYBMOFFW2KD2AJKV","created_at":"2026-05-18T12:28:33.132498+00:00"},{"alias_kind":"pith_short_8","alias_value":"IYBMOFFW","created_at":"2026-05-18T12:28:33.132498+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/IYBMOFFW2KD2AJKVPJH6VA4EVP","json":"https://pith.science/pith/IYBMOFFW2KD2AJKVPJH6VA4EVP.json","graph_json":"https://pith.science/api/pith-number/IYBMOFFW2KD2AJKVPJH6VA4EVP/graph.json","events_json":"https://pith.science/api/pith-number/IYBMOFFW2KD2AJKVPJH6VA4EVP/events.json","paper":"https://pith.science/paper/IYBMOFFW"},"agent_actions":{"view_html":"https://pith.science/pith/IYBMOFFW2KD2AJKVPJH6VA4EVP","download_json":"https://pith.science/pith/IYBMOFFW2KD2AJKVPJH6VA4EVP.json","view_paper":"https://pith.science/paper/IYBMOFFW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1410.0640&json=true","fetch_graph":"https://pith.science/api/pith-number/IYBMOFFW2KD2AJKVPJH6VA4EVP/graph.json","fetch_events":"https://pith.science/api/pith-number/IYBMOFFW2KD2AJKVPJH6VA4EVP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IYBMOFFW2KD2AJKVPJH6VA4EVP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IYBMOFFW2KD2AJKVPJH6VA4EVP/action/storage_attestation","attest_author":"https://pith.science/pith/IYBMOFFW2KD2AJKVPJH6VA4EVP/action/author_attestation","sign_citation":"https://pith.science/pith/IYBMOFFW2KD2AJKVPJH6VA4EVP/action/citation_signature","submit_replication":"https://pith.science/pith/IYBMOFFW2KD2AJKVPJH6VA4EVP/action/replication_record"}},"created_at":"2026-05-18T02:41:01.628202+00:00","updated_at":"2026-05-18T02:41:01.628202+00:00"}