{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:QPDKGECUGUQ5WBZ3N7H62YTYRH","short_pith_number":"pith:QPDKGECU","schema_version":"1.0","canonical_sha256":"83c6a310543521db073b6fcfed627889d7e1ca5ded3ecffa66257224e8cc51ba","source":{"kind":"arxiv","id":"1801.01453","version":1},"attestation_state":"computed","paper":{"title":"Adaptive kNN using Expected Accuracy for Classification of Geo-Spatial Data","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Andreas Hotho, Gerd Stumme, Juergen Mueller, Mark Kibanov, Martin Atzmueller, Martin Becker","submitted_at":"2017-12-14T14:09:06Z","abstract_excerpt":"The k-Nearest Neighbor (kNN) classification approach is conceptually simple - yet widely applied since it often performs well in practical applications. However, using a global constant k does not always provide an optimal solution, e.g., for datasets with an irregular density distribution of data points. This paper proposes an adaptive kNN classifier where k is chosen dynamically for each instance (point) to be classified, such that the expected accuracy of classification is maximized. We define the expected accuracy as the accuracy of a set of structurally similar observations. An arbitrary "},"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":"1801.01453","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2017-12-14T14:09:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5bbf0f39dda49bb14f17e63d251cec872d3071be850fbc9c81af8bed5ab92103","abstract_canon_sha256":"fe8e80f0039c26548231ec1559a72b2251c02298c56614866237436f5ca40899"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:26:42.072533Z","signature_b64":"TBne82O5LAJt68CkwsRPn9cSDezknktqOhE2QE6poLG6qvAp7J6PZ5KIdkvH6L7U+VG3WqVcLhbaQ5R8KxueCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"83c6a310543521db073b6fcfed627889d7e1ca5ded3ecffa66257224e8cc51ba","last_reissued_at":"2026-05-18T00:26:42.072120Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:26:42.072120Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive kNN using Expected Accuracy for Classification of Geo-Spatial Data","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Andreas Hotho, Gerd Stumme, Juergen Mueller, Mark Kibanov, Martin Atzmueller, Martin Becker","submitted_at":"2017-12-14T14:09:06Z","abstract_excerpt":"The k-Nearest Neighbor (kNN) classification approach is conceptually simple - yet widely applied since it often performs well in practical applications. However, using a global constant k does not always provide an optimal solution, e.g., for datasets with an irregular density distribution of data points. This paper proposes an adaptive kNN classifier where k is chosen dynamically for each instance (point) to be classified, such that the expected accuracy of classification is maximized. We define the expected accuracy as the accuracy of a set of structurally similar observations. An arbitrary "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.01453","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":"1801.01453","created_at":"2026-05-18T00:26:42.072171+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.01453v1","created_at":"2026-05-18T00:26:42.072171+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.01453","created_at":"2026-05-18T00:26:42.072171+00:00"},{"alias_kind":"pith_short_12","alias_value":"QPDKGECUGUQ5","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_16","alias_value":"QPDKGECUGUQ5WBZ3","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_8","alias_value":"QPDKGECU","created_at":"2026-05-18T12:31:39.905425+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/QPDKGECUGUQ5WBZ3N7H62YTYRH","json":"https://pith.science/pith/QPDKGECUGUQ5WBZ3N7H62YTYRH.json","graph_json":"https://pith.science/api/pith-number/QPDKGECUGUQ5WBZ3N7H62YTYRH/graph.json","events_json":"https://pith.science/api/pith-number/QPDKGECUGUQ5WBZ3N7H62YTYRH/events.json","paper":"https://pith.science/paper/QPDKGECU"},"agent_actions":{"view_html":"https://pith.science/pith/QPDKGECUGUQ5WBZ3N7H62YTYRH","download_json":"https://pith.science/pith/QPDKGECUGUQ5WBZ3N7H62YTYRH.json","view_paper":"https://pith.science/paper/QPDKGECU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.01453&json=true","fetch_graph":"https://pith.science/api/pith-number/QPDKGECUGUQ5WBZ3N7H62YTYRH/graph.json","fetch_events":"https://pith.science/api/pith-number/QPDKGECUGUQ5WBZ3N7H62YTYRH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QPDKGECUGUQ5WBZ3N7H62YTYRH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QPDKGECUGUQ5WBZ3N7H62YTYRH/action/storage_attestation","attest_author":"https://pith.science/pith/QPDKGECUGUQ5WBZ3N7H62YTYRH/action/author_attestation","sign_citation":"https://pith.science/pith/QPDKGECUGUQ5WBZ3N7H62YTYRH/action/citation_signature","submit_replication":"https://pith.science/pith/QPDKGECUGUQ5WBZ3N7H62YTYRH/action/replication_record"}},"created_at":"2026-05-18T00:26:42.072171+00:00","updated_at":"2026-05-18T00:26:42.072171+00:00"}