{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:QKBI4IZ7U5QXLR6NPVYXID7GF7","short_pith_number":"pith:QKBI4IZ7","canonical_record":{"source":{"id":"1905.11518","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-05-27T21:40:37Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b8ef0d7d0d8dbef5eef94c7b33786144c7f1c89aab5e2c7e9a8d5f6981714479","abstract_canon_sha256":"57920d6acb4922db0d2115bee1dbf219580a6303930123f1e43c64edc60f005d"},"schema_version":"1.0"},"canonical_sha256":"82828e233fa76175c7cd7d71740fe62ff9e9913737336bc4dffacdbfd745870c","source":{"kind":"arxiv","id":"1905.11518","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.11518","created_at":"2026-05-17T23:44:53Z"},{"alias_kind":"arxiv_version","alias_value":"1905.11518v1","created_at":"2026-05-17T23:44:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.11518","created_at":"2026-05-17T23:44:53Z"},{"alias_kind":"pith_short_12","alias_value":"QKBI4IZ7U5QX","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"QKBI4IZ7U5QXLR6N","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"QKBI4IZ7","created_at":"2026-05-18T12:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:QKBI4IZ7U5QXLR6NPVYXID7GF7","target":"record","payload":{"canonical_record":{"source":{"id":"1905.11518","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-05-27T21:40:37Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b8ef0d7d0d8dbef5eef94c7b33786144c7f1c89aab5e2c7e9a8d5f6981714479","abstract_canon_sha256":"57920d6acb4922db0d2115bee1dbf219580a6303930123f1e43c64edc60f005d"},"schema_version":"1.0"},"canonical_sha256":"82828e233fa76175c7cd7d71740fe62ff9e9913737336bc4dffacdbfd745870c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:53.174645Z","signature_b64":"Qj7jSzmC1C6DFzvBbv9s/xCLZXLpJsF89jhE5wE/ZRlRXqRzHpH1ZnuPeLg9m1EqOyj2X0XjcG8lLNAgj50vAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"82828e233fa76175c7cd7d71740fe62ff9e9913737336bc4dffacdbfd745870c","last_reissued_at":"2026-05-17T23:44:53.174103Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:53.174103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.11518","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":"2026-05-17T23:44:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jWFe8F23iNv5P4kNdWqimES10KP2ZZKwmU4cyhzU2YtLqDunL8DIJy1yaftXN9yoXBnwj1iDw5CaWmqgtV1HBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T21:00:56.504119Z"},"content_sha256":"0815544ff1d115a79216ba584dc8bf30dd28eb192dcdce2f531a9f978a47ead2","schema_version":"1.0","event_id":"sha256:0815544ff1d115a79216ba584dc8bf30dd28eb192dcdce2f531a9f978a47ead2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:QKBI4IZ7U5QXLR6NPVYXID7GF7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On a scalable problem transformation method for multi-label learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Dora Jambor, Peng Yu","submitted_at":"2019-05-27T21:40:37Z","abstract_excerpt":"Binary relevance is a simple approach to solve multi-label learning problems where an independent binary classifier is built per each label. A common challenge with this in real-world applications is that the label space can be very large, making it difficult to use binary relevance to larger scale problems. In this paper, we propose a scalable alternative to this, via transforming the multi-label problem into a single binary classification. We experiment with a few variations of our method and show that our method achieves higher precision than binary relevance and faster execution times on a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.11518","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:44:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FreqSPV13zOBraVEWB3ROXHjdeUFTR0pCUJd6QiaFRGs2HVhx4rDrZ7mryjfAgLx7IQCDnEVOXgUELID1X6OBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T21:00:56.504937Z"},"content_sha256":"819aa70c1304125c4cf4480d1ee45d8dc5c76a5928b54f0adca28ab5466d27fd","schema_version":"1.0","event_id":"sha256:819aa70c1304125c4cf4480d1ee45d8dc5c76a5928b54f0adca28ab5466d27fd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QKBI4IZ7U5QXLR6NPVYXID7GF7/bundle.json","state_url":"https://pith.science/pith/QKBI4IZ7U5QXLR6NPVYXID7GF7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QKBI4IZ7U5QXLR6NPVYXID7GF7/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":"2026-05-30T21:00:56Z","links":{"resolver":"https://pith.science/pith/QKBI4IZ7U5QXLR6NPVYXID7GF7","bundle":"https://pith.science/pith/QKBI4IZ7U5QXLR6NPVYXID7GF7/bundle.json","state":"https://pith.science/pith/QKBI4IZ7U5QXLR6NPVYXID7GF7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QKBI4IZ7U5QXLR6NPVYXID7GF7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:QKBI4IZ7U5QXLR6NPVYXID7GF7","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":"57920d6acb4922db0d2115bee1dbf219580a6303930123f1e43c64edc60f005d","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-05-27T21:40:37Z","title_canon_sha256":"b8ef0d7d0d8dbef5eef94c7b33786144c7f1c89aab5e2c7e9a8d5f6981714479"},"schema_version":"1.0","source":{"id":"1905.11518","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.11518","created_at":"2026-05-17T23:44:53Z"},{"alias_kind":"arxiv_version","alias_value":"1905.11518v1","created_at":"2026-05-17T23:44:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.11518","created_at":"2026-05-17T23:44:53Z"},{"alias_kind":"pith_short_12","alias_value":"QKBI4IZ7U5QX","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"QKBI4IZ7U5QXLR6N","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"QKBI4IZ7","created_at":"2026-05-18T12:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:819aa70c1304125c4cf4480d1ee45d8dc5c76a5928b54f0adca28ab5466d27fd","target":"graph","created_at":"2026-05-17T23:44:53Z","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"},"paper":{"abstract_excerpt":"Binary relevance is a simple approach to solve multi-label learning problems where an independent binary classifier is built per each label. A common challenge with this in real-world applications is that the label space can be very large, making it difficult to use binary relevance to larger scale problems. In this paper, we propose a scalable alternative to this, via transforming the multi-label problem into a single binary classification. We experiment with a few variations of our method and show that our method achieves higher precision than binary relevance and faster execution times on a","authors_text":"Dora Jambor, Peng Yu","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-05-27T21:40:37Z","title":"On a scalable problem transformation method for multi-label learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.11518","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":"sha256:0815544ff1d115a79216ba584dc8bf30dd28eb192dcdce2f531a9f978a47ead2","target":"record","created_at":"2026-05-17T23:44:53Z","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":"57920d6acb4922db0d2115bee1dbf219580a6303930123f1e43c64edc60f005d","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-05-27T21:40:37Z","title_canon_sha256":"b8ef0d7d0d8dbef5eef94c7b33786144c7f1c89aab5e2c7e9a8d5f6981714479"},"schema_version":"1.0","source":{"id":"1905.11518","kind":"arxiv","version":1}},"canonical_sha256":"82828e233fa76175c7cd7d71740fe62ff9e9913737336bc4dffacdbfd745870c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"82828e233fa76175c7cd7d71740fe62ff9e9913737336bc4dffacdbfd745870c","first_computed_at":"2026-05-17T23:44:53.174103Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:53.174103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Qj7jSzmC1C6DFzvBbv9s/xCLZXLpJsF89jhE5wE/ZRlRXqRzHpH1ZnuPeLg9m1EqOyj2X0XjcG8lLNAgj50vAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:53.174645Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.11518","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0815544ff1d115a79216ba584dc8bf30dd28eb192dcdce2f531a9f978a47ead2","sha256:819aa70c1304125c4cf4480d1ee45d8dc5c76a5928b54f0adca28ab5466d27fd"],"state_sha256":"7ea763e82dbd717c1f4f2b1eb9c9a939b0b5c021afa814d83776e50bda06c923"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UWj34dzYbw5kcb6P3cWb628ym3CBTYIuiceH1iapH0sfLbkt32Wz+wT5A5rppv7DqFHxMJbHB91LGPP2jORNDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T21:00:56.508692Z","bundle_sha256":"825cab2410750e005af7fd1835a8269decc811b70a824ccbc2ac62d0ee795136"}}