{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:EBB54Y4RORMO63VOOXE6LH62VU","short_pith_number":"pith:EBB54Y4R","schema_version":"1.0","canonical_sha256":"2043de63917458ef6eae75c9e59fdaad281e9ed2af8d2b5b4a088527d02d0344","source":{"kind":"arxiv","id":"2504.16268","version":2},"attestation_state":"computed","paper":{"title":"Boosting KNNClassifier Performance with Opposition-Based Data Transformation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Abdesslem Layeb","submitted_at":"2025-04-22T21:03:31Z","abstract_excerpt":"In this paper, we introduce a novel data transformation framework based on Opposition-Based Learning (OBL) to boost the performance of traditional classification algorithms. Originally developed to accelerate convergence in optimization tasks, OBL is leveraged here to generate synthetic opposite samples that enrich the training data and improve decision boundary formation. We explore three OBL variants Global OBL, Class-Wise OBL, and Localized Class-Wise OBL and integrate them with K-Nearest Neighbors (KNN). Extensive experiments conducted on 26 heterogeneous and high-dimensional datasets demo"},"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":"2504.16268","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-22T21:03:31Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"391ca3b4af3318c63d9a100bbffc35ab6e165fcfe633e024d4b0692951a27fa5","abstract_canon_sha256":"29651d2981a993fcbffac1facf22a44f0d87ed3f8791c87a813599b4995b8b2e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:53:54.853306Z","signature_b64":"q/AOdiQ4RKObbMr+WmrpFXkFSPvvZhoFFMbcCFKMYNadXlpJmXGrM4jwNuhW9Qs09mmpLPbWNuIRi2bX/yP9CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2043de63917458ef6eae75c9e59fdaad281e9ed2af8d2b5b4a088527d02d0344","last_reissued_at":"2026-07-05T10:53:54.852754Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:53:54.852754Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Boosting KNNClassifier Performance with Opposition-Based Data Transformation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Abdesslem Layeb","submitted_at":"2025-04-22T21:03:31Z","abstract_excerpt":"In this paper, we introduce a novel data transformation framework based on Opposition-Based Learning (OBL) to boost the performance of traditional classification algorithms. Originally developed to accelerate convergence in optimization tasks, OBL is leveraged here to generate synthetic opposite samples that enrich the training data and improve decision boundary formation. We explore three OBL variants Global OBL, Class-Wise OBL, and Localized Class-Wise OBL and integrate them with K-Nearest Neighbors (KNN). Extensive experiments conducted on 26 heterogeneous and high-dimensional datasets demo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.16268","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2504.16268/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2504.16268","created_at":"2026-07-05T10:53:54.852817+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.16268v2","created_at":"2026-07-05T10:53:54.852817+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.16268","created_at":"2026-07-05T10:53:54.852817+00:00"},{"alias_kind":"pith_short_12","alias_value":"EBB54Y4RORMO","created_at":"2026-07-05T10:53:54.852817+00:00"},{"alias_kind":"pith_short_16","alias_value":"EBB54Y4RORMO63VO","created_at":"2026-07-05T10:53:54.852817+00:00"},{"alias_kind":"pith_short_8","alias_value":"EBB54Y4R","created_at":"2026-07-05T10:53:54.852817+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/EBB54Y4RORMO63VOOXE6LH62VU","json":"https://pith.science/pith/EBB54Y4RORMO63VOOXE6LH62VU.json","graph_json":"https://pith.science/api/pith-number/EBB54Y4RORMO63VOOXE6LH62VU/graph.json","events_json":"https://pith.science/api/pith-number/EBB54Y4RORMO63VOOXE6LH62VU/events.json","paper":"https://pith.science/paper/EBB54Y4R"},"agent_actions":{"view_html":"https://pith.science/pith/EBB54Y4RORMO63VOOXE6LH62VU","download_json":"https://pith.science/pith/EBB54Y4RORMO63VOOXE6LH62VU.json","view_paper":"https://pith.science/paper/EBB54Y4R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.16268&json=true","fetch_graph":"https://pith.science/api/pith-number/EBB54Y4RORMO63VOOXE6LH62VU/graph.json","fetch_events":"https://pith.science/api/pith-number/EBB54Y4RORMO63VOOXE6LH62VU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EBB54Y4RORMO63VOOXE6LH62VU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EBB54Y4RORMO63VOOXE6LH62VU/action/storage_attestation","attest_author":"https://pith.science/pith/EBB54Y4RORMO63VOOXE6LH62VU/action/author_attestation","sign_citation":"https://pith.science/pith/EBB54Y4RORMO63VOOXE6LH62VU/action/citation_signature","submit_replication":"https://pith.science/pith/EBB54Y4RORMO63VOOXE6LH62VU/action/replication_record"}},"created_at":"2026-07-05T10:53:54.852817+00:00","updated_at":"2026-07-05T10:53:54.852817+00:00"}