{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:PEF532LGSTJYZLTXU3A4IQ4DEE","short_pith_number":"pith:PEF532LG","schema_version":"1.0","canonical_sha256":"790bdde96694d38cae77a6c1c443832136f6f82f2289aab378d459b11db50dfb","source":{"kind":"arxiv","id":"1409.0919","version":1},"attestation_state":"computed","paper":{"title":"Solving the Problem of the K Parameter in the KNN Classifier Using an Ensemble Learning Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ahmad Ali Alhasanat, Ahmad Basheer Hassanat, Ghada Awad Altarawneh, Mohammad Ali Abbadi","submitted_at":"2014-09-02T23:28:22Z","abstract_excerpt":"This paper presents a new solution for choosing the K parameter in the k-nearest neighbor (KNN) algorithm, the solution depending on the idea of ensemble learning, in which a weak KNN classifier is used each time with a different K, starting from one to the square root of the size of the training set. The results of the weak classifiers are combined using the weighted sum rule. The proposed solution was tested and compared to other solutions using a group of experiments in real life problems. The experimental results show that the proposed classifier outperforms the traditional KNN classifier "},"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":"1409.0919","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-09-02T23:28:22Z","cross_cats_sorted":[],"title_canon_sha256":"9a2072252f042e496591b513fb5dc9b77b42af9d716c3bdaaadef0291264d25e","abstract_canon_sha256":"817632c50a2adc6e67f54a763cbf01a50c25b5a9cf6a8f6412c6688b353abda0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:43:44.770228Z","signature_b64":"LX/mMOWqEFTo8WBDryWX1jfjVG1w0SX00maOcmzrBiFMCizrxlfsJ15v1LqA7RD2Jh7Swq4GLtcsX/IwAiNeCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"790bdde96694d38cae77a6c1c443832136f6f82f2289aab378d459b11db50dfb","last_reissued_at":"2026-05-18T02:43:44.769839Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:43:44.769839Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Solving the Problem of the K Parameter in the KNN Classifier Using an Ensemble Learning Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ahmad Ali Alhasanat, Ahmad Basheer Hassanat, Ghada Awad Altarawneh, Mohammad Ali Abbadi","submitted_at":"2014-09-02T23:28:22Z","abstract_excerpt":"This paper presents a new solution for choosing the K parameter in the k-nearest neighbor (KNN) algorithm, the solution depending on the idea of ensemble learning, in which a weak KNN classifier is used each time with a different K, starting from one to the square root of the size of the training set. The results of the weak classifiers are combined using the weighted sum rule. The proposed solution was tested and compared to other solutions using a group of experiments in real life problems. The experimental results show that the proposed classifier outperforms the traditional KNN classifier "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.0919","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":"1409.0919","created_at":"2026-05-18T02:43:44.769904+00:00"},{"alias_kind":"arxiv_version","alias_value":"1409.0919v1","created_at":"2026-05-18T02:43:44.769904+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.0919","created_at":"2026-05-18T02:43:44.769904+00:00"},{"alias_kind":"pith_short_12","alias_value":"PEF532LGSTJY","created_at":"2026-05-18T12:28:43.426989+00:00"},{"alias_kind":"pith_short_16","alias_value":"PEF532LGSTJYZLTX","created_at":"2026-05-18T12:28:43.426989+00:00"},{"alias_kind":"pith_short_8","alias_value":"PEF532LG","created_at":"2026-05-18T12:28:43.426989+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/PEF532LGSTJYZLTXU3A4IQ4DEE","json":"https://pith.science/pith/PEF532LGSTJYZLTXU3A4IQ4DEE.json","graph_json":"https://pith.science/api/pith-number/PEF532LGSTJYZLTXU3A4IQ4DEE/graph.json","events_json":"https://pith.science/api/pith-number/PEF532LGSTJYZLTXU3A4IQ4DEE/events.json","paper":"https://pith.science/paper/PEF532LG"},"agent_actions":{"view_html":"https://pith.science/pith/PEF532LGSTJYZLTXU3A4IQ4DEE","download_json":"https://pith.science/pith/PEF532LGSTJYZLTXU3A4IQ4DEE.json","view_paper":"https://pith.science/paper/PEF532LG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1409.0919&json=true","fetch_graph":"https://pith.science/api/pith-number/PEF532LGSTJYZLTXU3A4IQ4DEE/graph.json","fetch_events":"https://pith.science/api/pith-number/PEF532LGSTJYZLTXU3A4IQ4DEE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PEF532LGSTJYZLTXU3A4IQ4DEE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PEF532LGSTJYZLTXU3A4IQ4DEE/action/storage_attestation","attest_author":"https://pith.science/pith/PEF532LGSTJYZLTXU3A4IQ4DEE/action/author_attestation","sign_citation":"https://pith.science/pith/PEF532LGSTJYZLTXU3A4IQ4DEE/action/citation_signature","submit_replication":"https://pith.science/pith/PEF532LGSTJYZLTXU3A4IQ4DEE/action/replication_record"}},"created_at":"2026-05-18T02:43:44.769904+00:00","updated_at":"2026-05-18T02:43:44.769904+00:00"}