{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:UHDWYOEEL76KENZ3QJFXM5GQQJ","short_pith_number":"pith:UHDWYOEE","schema_version":"1.0","canonical_sha256":"a1c76c38845ffca2373b824b7674d0825992667a5fd64a69b99cd95f25ed3bc5","source":{"kind":"arxiv","id":"1602.07464","version":1},"attestation_state":"computed","paper":{"title":"Feature ranking for multi-label classification using Markov Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Pawe{\\l} Teisseyre","submitted_at":"2016-02-24T11:11:10Z","abstract_excerpt":"We propose a simple and efficient method for ranking features in multi-label classification. The method produces a ranking of features showing their relevance in predicting labels, which in turn allows to choose a final subset of features. The procedure is based on Markov Networks and allows to model the dependencies between labels and features in a direct way. In the first step we build a simple network using only labels and then we test how much adding a single feature affects the initial network. More specifically, in the first step we use the Ising model whereas the second step is based on"},"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":"1602.07464","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-02-24T11:11:10Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"db168673515583a4bd231ad9c9db9fc9988cbff7a884bfef1418163e1d9327cd","abstract_canon_sha256":"4d01db7342af31c121bbe24e05e2d46a121c8e251ac36578a6ef3cd7cfe499af"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:02.953646Z","signature_b64":"dFjO6Kiqd30P9GPGG/qEJvq4BwNLc5CP02+rpCqj/DVFn2h3uuPmuvWg5PFakEoAZ+a2/O26lZjn/aTzrXJEDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a1c76c38845ffca2373b824b7674d0825992667a5fd64a69b99cd95f25ed3bc5","last_reissued_at":"2026-05-18T01:20:02.953037Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:02.953037Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Feature ranking for multi-label classification using Markov Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Pawe{\\l} Teisseyre","submitted_at":"2016-02-24T11:11:10Z","abstract_excerpt":"We propose a simple and efficient method for ranking features in multi-label classification. The method produces a ranking of features showing their relevance in predicting labels, which in turn allows to choose a final subset of features. The procedure is based on Markov Networks and allows to model the dependencies between labels and features in a direct way. In the first step we build a simple network using only labels and then we test how much adding a single feature affects the initial network. More specifically, in the first step we use the Ising model whereas the second step is based on"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.07464","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":""},"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":"1602.07464","created_at":"2026-05-18T01:20:02.953128+00:00"},{"alias_kind":"arxiv_version","alias_value":"1602.07464v1","created_at":"2026-05-18T01:20:02.953128+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.07464","created_at":"2026-05-18T01:20:02.953128+00:00"},{"alias_kind":"pith_short_12","alias_value":"UHDWYOEEL76K","created_at":"2026-05-18T12:30:46.583412+00:00"},{"alias_kind":"pith_short_16","alias_value":"UHDWYOEEL76KENZ3","created_at":"2026-05-18T12:30:46.583412+00:00"},{"alias_kind":"pith_short_8","alias_value":"UHDWYOEE","created_at":"2026-05-18T12:30:46.583412+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/UHDWYOEEL76KENZ3QJFXM5GQQJ","json":"https://pith.science/pith/UHDWYOEEL76KENZ3QJFXM5GQQJ.json","graph_json":"https://pith.science/api/pith-number/UHDWYOEEL76KENZ3QJFXM5GQQJ/graph.json","events_json":"https://pith.science/api/pith-number/UHDWYOEEL76KENZ3QJFXM5GQQJ/events.json","paper":"https://pith.science/paper/UHDWYOEE"},"agent_actions":{"view_html":"https://pith.science/pith/UHDWYOEEL76KENZ3QJFXM5GQQJ","download_json":"https://pith.science/pith/UHDWYOEEL76KENZ3QJFXM5GQQJ.json","view_paper":"https://pith.science/paper/UHDWYOEE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1602.07464&json=true","fetch_graph":"https://pith.science/api/pith-number/UHDWYOEEL76KENZ3QJFXM5GQQJ/graph.json","fetch_events":"https://pith.science/api/pith-number/UHDWYOEEL76KENZ3QJFXM5GQQJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UHDWYOEEL76KENZ3QJFXM5GQQJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UHDWYOEEL76KENZ3QJFXM5GQQJ/action/storage_attestation","attest_author":"https://pith.science/pith/UHDWYOEEL76KENZ3QJFXM5GQQJ/action/author_attestation","sign_citation":"https://pith.science/pith/UHDWYOEEL76KENZ3QJFXM5GQQJ/action/citation_signature","submit_replication":"https://pith.science/pith/UHDWYOEEL76KENZ3QJFXM5GQQJ/action/replication_record"}},"created_at":"2026-05-18T01:20:02.953128+00:00","updated_at":"2026-05-18T01:20:02.953128+00:00"}