{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:RBPZBXBZXZGEKUBNPAO3T2UZFW","short_pith_number":"pith:RBPZBXBZ","schema_version":"1.0","canonical_sha256":"885f90dc39be4c45502d781db9ea992daf3c5ee0b34a9276e699f9fd8b7dfb83","source":{"kind":"arxiv","id":"1610.09778","version":1},"attestation_state":"computed","paper":{"title":"DPPred: An Effective Prediction Framework with Concise Discriminative Patterns","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jian Peng, Jiawei Han, Jinfeng Xiao, Jingbo Shang, Meng Jiang, Wenzhu Tong","submitted_at":"2016-10-31T03:43:04Z","abstract_excerpt":"In the literature, two series of models have been proposed to address prediction problems including classification and regression. Simple models, such as generalized linear models, have ordinary performance but strong interpretability on a set of simple features. The other series, including tree-based models, organize numerical, categorical and high dimensional features into a comprehensive structure with rich interpretable information in the data.\n  In this paper, we propose a novel Discriminative Pattern-based Prediction framework (DPPred) to accomplish the prediction tasks by taking their 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":"1610.09778","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-10-31T03:43:04Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6cf7fd29ba377ee5cdad26f724990cbf9e90e15134534079acc6eff1cf6bb356","abstract_canon_sha256":"1253256bde6ae62fb5b384f178040e801014b1d95e6ec708301126a7638d1cdb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:00:47.205722Z","signature_b64":"nDdeuo7O+Mw1E0jGn8VotuehHmCZ77PMj4lBJnqAD6vNf6TLpMauRclYd0VOzqFOJSqIis5T2u4fPc9QAReuCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"885f90dc39be4c45502d781db9ea992daf3c5ee0b34a9276e699f9fd8b7dfb83","last_reissued_at":"2026-05-18T01:00:47.205175Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:00:47.205175Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DPPred: An Effective Prediction Framework with Concise Discriminative Patterns","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jian Peng, Jiawei Han, Jinfeng Xiao, Jingbo Shang, Meng Jiang, Wenzhu Tong","submitted_at":"2016-10-31T03:43:04Z","abstract_excerpt":"In the literature, two series of models have been proposed to address prediction problems including classification and regression. Simple models, such as generalized linear models, have ordinary performance but strong interpretability on a set of simple features. The other series, including tree-based models, organize numerical, categorical and high dimensional features into a comprehensive structure with rich interpretable information in the data.\n  In this paper, we propose a novel Discriminative Pattern-based Prediction framework (DPPred) to accomplish the prediction tasks by taking their a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.09778","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":"1610.09778","created_at":"2026-05-18T01:00:47.205238+00:00"},{"alias_kind":"arxiv_version","alias_value":"1610.09778v1","created_at":"2026-05-18T01:00:47.205238+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.09778","created_at":"2026-05-18T01:00:47.205238+00:00"},{"alias_kind":"pith_short_12","alias_value":"RBPZBXBZXZGE","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_16","alias_value":"RBPZBXBZXZGEKUBN","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_8","alias_value":"RBPZBXBZ","created_at":"2026-05-18T12:30:41.710351+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/RBPZBXBZXZGEKUBNPAO3T2UZFW","json":"https://pith.science/pith/RBPZBXBZXZGEKUBNPAO3T2UZFW.json","graph_json":"https://pith.science/api/pith-number/RBPZBXBZXZGEKUBNPAO3T2UZFW/graph.json","events_json":"https://pith.science/api/pith-number/RBPZBXBZXZGEKUBNPAO3T2UZFW/events.json","paper":"https://pith.science/paper/RBPZBXBZ"},"agent_actions":{"view_html":"https://pith.science/pith/RBPZBXBZXZGEKUBNPAO3T2UZFW","download_json":"https://pith.science/pith/RBPZBXBZXZGEKUBNPAO3T2UZFW.json","view_paper":"https://pith.science/paper/RBPZBXBZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1610.09778&json=true","fetch_graph":"https://pith.science/api/pith-number/RBPZBXBZXZGEKUBNPAO3T2UZFW/graph.json","fetch_events":"https://pith.science/api/pith-number/RBPZBXBZXZGEKUBNPAO3T2UZFW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RBPZBXBZXZGEKUBNPAO3T2UZFW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RBPZBXBZXZGEKUBNPAO3T2UZFW/action/storage_attestation","attest_author":"https://pith.science/pith/RBPZBXBZXZGEKUBNPAO3T2UZFW/action/author_attestation","sign_citation":"https://pith.science/pith/RBPZBXBZXZGEKUBNPAO3T2UZFW/action/citation_signature","submit_replication":"https://pith.science/pith/RBPZBXBZXZGEKUBNPAO3T2UZFW/action/replication_record"}},"created_at":"2026-05-18T01:00:47.205238+00:00","updated_at":"2026-05-18T01:00:47.205238+00:00"}